Forecasting future scenarios of coastline changes in Turkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043)

Forecasting future scenarios of coastline changes in Turkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forecasting future scenarios of coastline changes in Turkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043) Münevver Gizem GÜMÜŞ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4411235/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2024 Read the published version in Earth Science Informatics → Version 1 posted 11 You are reading this latest preprint version Abstract Complex changes in coastlines are increasing with climate, sea level, and human impacts. Remote Sensing (RS) and Geographic Information Systems (GIS) provide critical information to rapidly and precisely monitor environmental changes in coastal areas and to understand and respond to environmental, economic, and social impacts. This study was aimed at determining the temporal changes in the coastline of the Seyhan Basin, which is one of the basins significantly affected by climate change and drought in Turkiye. In this context, approximately 50 km of coastline was automatically extracted on the Google Earth Engine (GEE) platform using Landsat satellite images from 1985–2023. This coastline was divided into 3 different regions, and spatial analysis was performed with different statistical proportioning techniques (EPR, LRR, NSM, SCE, and WLR) according to years with the Digital Shoreline Analysis System (DSAS) tool. In addition, to determine whether there is a statistically significant difference between the results obtained from the different methods used to determine the coastal change, the Kruskal-Wallis H test and ANOVA test were applied by min-max normalization. The amounts of erosion and deposition found according to different methods vary by region. Statistical differences were found between the methods used, varying by region. In general, NSM and EPR methods provided similar results in determining coastal changes, while other methods differed by region. In the study, the Kalman filtering model was also used to predict the coastline for the years 2033 and 2043 and to identify areas that are vulnerable to erosion and deposition on the future coastline. Comparisons were made to determine the performance of Kalman filtering. In the 10-year and 20-year future forecasts for determining the coastline for the years 2033 and 2043 with the Kalman filtering model, it was determined that the excessive prediction time negatively affected the performance in determining the coastal boundary changes. DSAS Earth Engine GIS Kalman Filter Kruskal-Wallis H Test Shoreline Changes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Coastal zones are of vital importance in many aspects, such as biodiversity conservation, sustainable management of water resources, economic activities, and human settlements (Vernberg and Vernberg 2001 ; Hinrichsen 2013 ). Coasts stand out as dynamic areas constantly evolving through natural processes and human intervention (Çolak 2024 ; Farris et. al. 2023 ; Alesheikh et al. 2007 ; Schwartz 2006 ). Factors such as disaster risks in coastal areas (global climate change, sea level rise, coastal erosion), rapid and uncontrolled construction of coasts, location confusion between different sectors, loss of natural resources in coastal areas, deterioration of archaeological, historical, and cultural values are among the factors that constantly affect coastlines (French 2001 ). A detailed study of these changes is important from environmental, economic, and social perspectives. Research into the causes, impacts, and management of coastal change plays an important role in the sustainable use and conservation of coastal areas. Conventional field survey methods, such as this study, are time-consuming, costly, and labor-intensive for a large coastline survey (Masek et. al. 2006 ; Kundu and Mandal 2024 ). For effective understanding and management of these processes, the use of digital coastal inference methods, UA and GIS technologies offers many advantages (Li et al. 2022 ; Skilodimou et al. 2021 ; Dua et al. 2021 ). UA provides the capacity to remotely monitor and analyze coastal changes. GIS provides a powerful tool to organize, analyze, and visualize complex geographic data. The effective use of these technologies helps us to better understand changes in coastal zones, predict future impacts, and develop sustainable management strategies (Huang et. al. 2019 ; Davidson et al. 2010 ). The analysis of coastal changes, when integrated with UA and GIS technologies, allows us to make stronger and more scientifically based decisions on issues such as disaster risk reduction, wildlife conservation, and effective management of tourism activities (Kafrawy and Ahmed 2020 ). Furthermore, these technologies play a critical role in the process of continuously monitoring and analyzing coastal changes to improve the quality of life of people living in coastal areas and to secure their economic well-being sustainably. Many methods and techniques have been developed in the literature to analyze coastal environmental changes. In this context, the study of coastal topographic features and morphological changes is an important first step to understanding coastal dynamics (Karunarathna et al. 2018 ). This can be accomplished using topographic maps and satellite imagery to assess processes such as coastal erosion, coastal accretion, and sedimentation. The study of hydrological factors such as coastal water movement, tides, and currents is important to understand the movement and interaction of water bodies in coastal areas (Thakur et al. 2017 ; Acciarri et al. 2016 ). These analyses include parameters such as river flow, water temperature, and salinity (Abd-Elhamid et al. 2023 ). The study of coastal sedimentary processes and sediment properties is used to understand beach and seafloor changes (Goudie 2018 ; Miliman 1980 ; Brandt 2000 ; Williams and Wolman 1984 ). Analysis of sediment samples provides information on sediment transport and storage (Tagil and Cürebal 2005 ). There are different studies in the literature on digital coastline analysis and future prediction using time series. Kundu and Mandal ( 2024 ) focused on the detection of shoreline change and future prediction of the Sundarban Delta using a digital shoreline analysis system with the help of multispectral satellite imagery and geographic information systems. Palanisamy et. al. ( 2024 ) analyzed 60 km of shoreline changes on the south coast of Rio de Janeiro from 1986 to 2018 using satellite imagery and statistically determined the rates of change. They also identified projected coastal locations for 2032–2042, emphasizing that human activities can affect coastal patterns, protective measures can affect erosion and deposition patterns, coastal development activities can disrupt natural processes, and effective coastal management strategies are essential for the protection of ecosystems and communities. Ataol et al. ( 2019 ), in their study, aimed to determine the erosion rates in the Kızılırmak delta river mouth, which is subjected to geomorphological changes due to the large dams built on it, and as a result, it is observed that the dams built near the delta cause an increase in erosion on the coastline. As observed all over the world, external interventions such as structures built in the rivers belonging to deltas in Turkiye continuously threaten delta coastal boundary changes (Ataol et al. 2019 ; Kuleli et al. 2011 ). Especially the Mediterranean basin is considered to be one of the most vulnerable regions affected by climate change (Nicholls and Hoozemans 1996 ; Lionello et al. 2006 ). In this context, it is observed that in our country, especially on the Mediterranean delta coasts, change detection studies on coastlines are increasing (Ozaner 1993 ; Kazı and Karabulut 2023 ; Ciritci and Türk 2020 ; Kılar and Çiçek 2019 ; Ataol and Kale 2022 müs et al. 2022 ). In this context, understanding and responding appropriately to changes over time in critical coastal areas such as the Seyhan Basin in Turkiye, the study area, is critical for environmental sustainability and community resilience. This paper aims to analyze the geometric changes in the coastline of the Seyhan Basin in Turkiye over time using DSAS V.5.0 (2021) from a scientific perspective and to make future predictions. The paper consists of five main phases. In the first stage, using the GEE ( 2009 ) platform, automatic extraction of the coastline in vector format using Landsat satellite imagery between 1985 and 2023 (approximately 40 years) will be determined using the Automated Water Extraction Index (AWEI). In the second stage, all data will be exported and processed in the ArcGIS environment, and coastal change rates will be calculated using different statistical methods with the DSAS tool. In the third stage, 10- and 20-year performance analyses will be performed to evaluate the statistical accuracy of the Kalman filter method used in the future prediction of the coastline. In the fourth stage, coastline forecasts will be made for the years 2033–2043 using the Kalman filter model based on the analyses obtained. In the fifth stage, the Kruskal-Wallis H test and ANOVA test will be used to determine whether there is a statistically significant difference between the results obtained using different methods of coastal change. In this way, it is aimed at establishing a scientific basis by statistically analyzing coastal boundary changes depending on method and time to reduce environmental risks in the region, protect natural resources, and support sustainable development in coastal areas. 2. Materials and methods In this paper, the computational power of DSAS and GEE was used to analyze the temporal change along the coastline. By downloading the annual median composites of satellite imagery through the GEE platform, it is aimed at performing automatic coastline extraction. The shoreline extraction was analyzed using the Landsat collection of medium spatial resolution and multi-temporal satellite imagery from the United States Geological Survey (USGS) Earth Explorer archive (USGS 2008 ). Landsat imagery plays an important role in coastline inference because this type of remote sensing data allows for a detailed analysis of coastal dynamics by delineating the boundaries between water and land (Marfai et al. 2008 ; Teodoro 2016 ). The satellite imagery collections and their technical specifications used for coastal change analysis over 40 years between 1985 and 2023 are presented in Table 1 . The technical workflow diagram of the study is presented in Fig. 1 . Table 1 Satellite image collections and technical specifications used in the study Years Collection name Collection Snippet Scene size Sensor Resolution Path/Row 1985/1990/1995/2000/2005/2010 USGS Landsat 5 Level 2, Collection 2, Tier 1 ee.ImageCollection ("LANDSAT/LT05/C02/T1_L2") 170 km x 183km TM 30 m 175/034–175/035–176/034 2015/2020/2023 USGS Landsat 8 Level 2, Collection 2, Tier 1 ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") 170 km x 183km OLI 30 m 175/034–175/035–176/034 2.1. Study area In this paper, the Seyhan Basin in Turkiye is used as the study area. The Seyhan Basin is an important geographical region in southern Turkiye, located southwest of the Çukurova delta complex in the Eastern Mediterranean region. The basin extends along the Seyhan River from Sivas to the eastern Mediterranean Sea, with a basin area of 22,035 km2, representing 2.07% of Turkiye's land area. The Seyhan River is one of the largest of Turkiye's rivers flowing into the Mediterranean Sea with a length of 560 km and a catchment area of 20450 km2. The coastline of the Seyhan Basin has changed over time due to geographical features, climatic influences, and human activities. These changes have had impacts on ecosystems in the coastal zone and shaped the use of water resources. The geographical location of the basin is in the form of a wedge extending northward from Çukurova. Seyhan Basin is located between 36 o 30' and 39 o 15' north latitude and 34 0 45' and 37 0 00' east longitude (Fig. 2 ). This basin, whose soil structure is generally mountainous, starts from the low and flat Çukurova base in the south and continues with high mountainous areas and hilly sections towards the north. The climate in the coastal parts of the basin is hot and dry in summers and mild and rainy in winters. The vegetation of the basin varies depending on climate, geology, soil, and landforms. In the arid northern parts, grasses and meadows, and occasionally oak and oak shrubs are found, while shrub and maquis communities specific to the Mediterranean climate are encountered as you go south. Seyhan Basin is one of the important regions of Turkiye with its rich water resources, diverse climatic characteristics, and agricultural potential. In the study, the coastal region was divided into three main zones to interpret the results of the analysis in a more qualified regional sense. The first zone is the lower coastal zone of Akyatan Lagoon, a Ramsar site. The second zone defines the lower coastal zone of Tuzla Lake. The third zone is defined as the Seyhan River, the mouth of the delta where the river meets the sea. 2.2 Coastal boundary determination GEE, which was used in this study for automatic extraction of the Seyhan basin coastline in vector format, is a JavaScript-based platform that provides a user-friendly tool for web-based analysis. This facilitates fast processing and analysis of environmental data. The popularity and easy learnability of JavaScript allow users to quickly adapt to the platform (Pano et al. 2018 ). Furthermore, JavaScript's dynamic type system supports rapid prototyping and the development of code. The flexibility of the platform makes it compatible with various web-based technologies and libraries, which allows it to easily integrate external data sources and analysis tools. In this study, the GEE platform was effectively utilized for the definition of input parameters and the automatic extraction of coastal boundary lines. In addition, AWEI, an index developed in 2014 (Feyisa et al. 2014 ), was used in this study to automatically distinguish between water and land from satellite imagery. This index is derived from satellite imagery, especially Landsat, and is widely used to detect water surfaces (Guo et al. 2017 ; Fisher et al. 2016 ; Isiacik Colak et al. 2019 ; Wicaksono and Wicaksono 2019 ). AWEI highlights differences between water and land using a combination of pixel values in various spectral bands. It works specifically on spectral bands such as blue, green, red, and near-infrared and calculates the relationship between pixel values in these bands. The mathematical formula is as in equations 1 and 2 : $${AWEI}_{TM}= 4*({ {\rho }}_{Band2}-{ {\rho }}_{Band5})-(0.25*{ {\rho }}_{Band4}+2.75*{ {\rho }}_{Band7})$$ 1 where ρ is the reflectance value of the spectral bands of Landsat 5 TM: band 2 (green), band 4 (NIR), band 5 (SWIR), and band 7 (SWIR). $${AWEI}_{OLI}= 4*({ {\rho }}_{Band3}-{ {\rho }}_{Band6})-(0.25*{ {\rho }}_{Band5}+2.75*{ {\rho }}_{Band7})$$ 2 where ρ is the reflectance value of the spectral bands of Landsat 8 OLI: band 3 (green), band 5 (NIR), band 6 (SWIR), and band 7 (SWIR). Each pixel value contributes to the calculation of the index by multiplying it by a specific weight. AWEI is used as an important tool in coastal boundary extraction. By emphasizing the distinct differences between water and land, this index provides an effective method to accurately identify water surfaces (Bishop-Taylor et al. 2019 ; Figliomeni et al. 2023 ). 2.3. Kalman filtering and statistical analysis in DSAS DSAS (2022) is an add-on software tool to ArcGIS used in coastal areas and is designed to analyze coastal changes. DSAS makes it possible to perform spatial analysis with different statistical rate techniques using digital data from the shoreline. End Point Rate (EPR) calculates the rate of shoreline change between two-time points. It is a very common method used in the literature in coastal boundary change studies. This method describes coastal change using the direct distance between the start and endpoints. Net Shoreline Movement (NSM) measures the net change of the shoreline over a given time interval. It assesses coastal change by calculating the difference between the start and endpoints. The Shoreline Change Envelope (SCE) assesses the curvature of the shoreline. It identifies changes in coastal morphology by measuring the curvature along the shoreline. Linear Regression Rate (LRR) is a least squares method that determines how linearly the shoreline changes over time. This method determines the trend of coastal change using time-series data. It uses distance and time as variables. Weighted Linear Regression (WLR) weighted the temporal change of the coastline. This method analyzes coastal change by assigning different weights to the rates of change over different periods. In weighted linear regression analysis, the importance of data points is related to measurement uncertainty. More reliable data are given more weight, i.e., points with lower measurement uncertainty are more important. The weight (w) is defined as the inverse square of the measurement uncertainty, i.e., the smaller e, the greater the weight. In the study, all coastlines were considered in the analysis process, and distance and time were used as variables. Analyzing the change of the coastline over time and predicting the future state is critical for coastal management and planning. Coastal management experts and planners need reliable and effective tools to predict the future position of the coastline. DSAS v.5 uses a Kalman filter-based approach to shoreline forecasting. This approach offers the ability to predict shoreline positions for the next 10 and 20 years based on historical shoreline position data (Kalman 1960 ). The DSAS Kalman Filter Technique is initialized using a calculated linear regression ratio. This ratio is determined using a linear regression analysis of historical shoreline data. It then calculates the position and rate of change of the shoreline at a step interval of one-tenth of each year (Long and Plant 2012 ). The forecasting process involves the Kalman Filter analyzing the difference between modeled and observed shoreline positions, adding speed and uncertainty to the predictions. However, this approach has limited capacity to deal with uncertainties and may not adequately reflect some complex coastal dynamics. Also, if there are fewer than four coastline data, accurate forecasts may be difficult (Ciritci and Turk 2020). In this study, coastlines from nine different years were used as input data. The shoreline forecasts for the years 2033 and 2043 were made with the Beta Forecast tool using the Kalman Filter Model in DSAS. The forecasted coastline data were mapped together with uncertainty polygons. 2.4 Kruskal-Wallis H Test and analysis of Variance (ANOVA) The main criterion for determining the correct method for statistical data analysis is the type of data. Sample size, normal distribution of the data, and homogeneity of variance play an important role in determining whether the data is parametric or nonparametric. If the data set has a sample size of more than 30, a normal distribution, and a homogeneity of variance, it is parametric. For parametric data, a t-test (two groups) or ANOVA (more than two groups) is used. For nonparametric data, chi-square tests are used to compare between two groups, and the Kruskal-Wallis H test is preferred if the number of groups is more than two. In this study, to determine the temporal and geometric changes in the coastline of the Seyhan basin, which is significantly affected by climate change and drought in Turkiye, spatial analyses were carried out over the years using statistical techniques such as EPR, LRR, NSM, SCE, and WLR with the DSAS tool. With these techniques, it was tried to determine whether the results obtained from the changes in the coastline using different perspectives are statistically significant, that is, whether they contain similar results. If the Kruskal-Wallis H test or ANOVA results show that there is a difference between the groups, multiple comparisons should be made to determine which groups this difference originates from. If sample size, normal distribution, and homogeneity of variance are provided, Tukey's test can be used; if not, Tamhane's T2 can be used to determine which groups are different. SPSS software was used in these analyses. SPSS obtains the p-value at the 95% significance level from the F table. If the p-value obtained is less than 0.05, it can be concluded that there are statistically significant differences between the variables (Kalaycı 2010 ). Within the framework of this methodology, we adopted an approach focused on selecting appropriate analysis methods to assess the statistical significance of the results obtained. The data set for each technique was examined in detail to determine whether it had a normal distribution and homogeneous variance. This assessment was carried out to ensure the reliability and generalizability of the statistical results. To understand the effects of statistical measurement methods on the data set and to increase the reliability of the results, normal distribution and variance homogeneity analyses were performed. In this study, the Kruskal-Wallis H test, ANOVA, and multiple comparison analyses were performed to determine whether the temporal and geometric changes in the coastline in the Seyhan basin obtained from different techniques are statistically significant. 2.5 Min-Max normalization Min-max normalization contributes to the evaluation of data in different units. This method expresses each feature in the data set on the same scale. Features measured in different units or varying in different ranges become comparable because they are expressed on the same scale, thanks to this normalization technique. Features measured in different units are on the same scale when Min-Max normalization is applied. This ensures that each feature contributes equally during the training of the model. For example, if one feature is measured in meters and the other feature is measured in miles, thanks to Min-Max normalization, they will both be in the range (0, 1) and will contribute similarly to the training process. Having values in different units on the same scale allows for more accurate comparisons between them. However, it is important to consider whether the normalization is appropriate depending on the data set and the algorithms used. The formula for Min-Max normalization is as in Eq. 3 (Jain et al. 2005 ): $${X}_{normalized}=\frac{X-{X}_{min}}{{X}_{max}-{X}_{min}}$$ 3 X = original feature value, X_min = the minimum value of the property, X_max = the maximum value of the property, X_normalized = normalized feature value. In this study, NSM and SCE are the distance measurement models, and EPR, LRR, and WLR are the statistical measurement methods. Deposition and erosion values obtained by statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR are in meters or meters/year. Min-max normalization was applied to statistically compare the results obtained from these techniques. 3. Results and discussion To determine the temporal and geometric changes in the coastline of the Seyhan basin in Turkiye, which is significantly affected by climate change and drought in Turkiye, comparative statistical and performance analyses were applied to the results obtained with statistical proportioning techniques such as different EPR, LRR, NSM, SCE, and WLR with the DSAS tool. The coastline of this basin is divided into three main regions. In addition, by using the Kalman filtering model, the shorelines of the years 2033 and 2043 were predicted, and the vulnerable areas in terms of erosion and deposition on the future shoreline were determined. Again, the Kruskal-Wallis H test and ANOVA tests were used to determine whether there is a statistically significant difference between the results obtained using different methods of coastal change. All the findings obtained in the study are explained in detail under the sub-headings in this section. 3.1. Coastal change analysis results In this paper, as the first step in defining the input parameters, the GEE platform was used to automatically extract the coastline of the 560 km-long Seyhan Basin in vector format using satellite images from 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2023 every five years (Fig. 3 ). Landsat satellite data collections were used for satellite images. The boundary geometry of the study area was determined from the image collection, and the images were filtered by defining other necessary parameters (cloudiness, snow cover ratio, and date). Image composites were created for each year by using the annual median composites of the available satellite images in a one-year time interval as the date. Cloud masking was performed on the filtered images to enhance the images. In the second step, AWEI, an index developed to automatically distinguish between water and land from satellite images, was used in the coastal extraction process. The Otsu thresholding method, which can automatically threshold the generated images, was used to separate the image into two different classes (binary images). Coastal boundaries were automatically extracted in vector format (.shp) from the obtained images on the platform. The entire data set was exported and transferred to the ArcGIS 10.6 environment, and the third step, the DSAS modeling phase, was started. First, a personal database was created, and the entire data set was stored here. Then a baseline was created for the analysis with buffer analysis. This baseline provides a basic reference point for the calculation of coastal changes. Using the baseline, 970 transect lines were created on the shorelines at 50-meter intervals. All these input data were processed into the model, and EPR, LRR, NSM, and WLR analysis results were calculated and a table was created. Negative values in the analysis results define the presence of erosion, and positive values define coastal accretion. To define this change between 1985 and 2023 more clearly, the study area was represented by three zones according to block boundaries. All statistical results are presented in Figs. 4– 6 for each zone. To better interpret the EPR, LRR, WLR, and NSM analysis result data and graphic values presented above, they were evaluated in 7 basic classes. In the classification, red indicates extremely high erosion, orange indicates severe erosion, light orange indicates moderate erosion, yellow indicates no change areas, light green indicates moderate coastal deposition, green indicates severe deposition, and dark green indicates extremely severe deposition. SCE analysis result data and graphic values were evaluated in four basic classes. No change zones are shown in dark green, moderate change in green, severe change in yellow, and very severe change zones in red. The table below (Table 2 ) shows the results of EPR, LRR, WLR, SCE, and NSM model analysis for the shoreline change rates in the Seyhan basin coast from 1985 to 2023. Table 2 Shoreline change rates obtained from EPR, LRR, NSM, WLR, and SCE analyses (1985–2023) Transect SCE* (m) NSM* (m) EPR* (m/yr) LRR* (m/yr) WLR* (m/yr) No Min Max Mean Min Max Mean Min. Max. Mean Min Max Mean Min Max Mean ZONE 1 0–50 0.0 1022.1 105.5 -43.3 997.7 82.5 -1.1 26.3 2.2 -1.4 5.6 0.5 -0.4 10.4 0.8 51–100 19.2 1430.6 496.5 -31.6 1382.4 456.3 -0.8 36.4 12.0 -0.8 9.0 2.3 -0.9 14.3 4.4 101–150 8.6 33.2 20.1 -24.9 -8.6 -17.3 -0.7 -0.2 -0.5 -0.7 -0.2 -0.5 -0.8 -0.2 -0.5 151–200 0.1 25.4 13.4 -23.6 0.0 -12.3 -0.6 0.0 -0.3 -0.5 0.0 -0.3 -0.6 0.0 -0.3 201–250 11.0 26.4 20.3 -26.0 -11.0 -18.4 -0.7 -0.3 -0.5 -0.8 -0.2 -0.5 -0.9 -0.2 -0.5 251–300 16.0 45.5 30.1 -44.3 -4.1 -21.9 -1.2 -0.1 -0.6 -1.0 -0.2 -0.7 -1.4 -0.3 -0.8 301–350 33.2 67.5 48.6 -63.6 -26.7 -41.9 -1.7 -0.7 -1.1 -1.6 -0.9 -1.2 -2.2 -0.8 -1.3 351–400 43.3 76.4 60.5 -76.4 -39.2 -59.6 -2.0 -1.0 -1.6 -1.8 -1.1 -1.5 -1.9 -0.8 -1.5 401–450 30.2 75.7 56.8 -75.7 -30.0 -56.3 -2.0 -0.8 -1.5 -1.8 -1.0 -1.5 -1.8 -0.9 -1.5 451–492 26.9 68.2 55.1 -68.2 -26.8 -52.6 -1.8 -0.7 -1.4 -1.7 -0.6 -1.5 -2.0 -0.6 -1.6 ZONE 2 493–550 14.6 94.8 73.5 -91.0 17.8 -71.8 -2.4 0.5 -1.9 -2.5 0.0 -2.0 -3.4 0.4 -2.1 551–600 37.4 68.2 52.2 -68.2 -37.4 -51.7 -1.8 -1.0 -1.4 -1.8 -0.9 -1.4 -2.0 -0.9 -1.5 601–650 6.2 41.4 25.0 -41.4 -2.4 -20.6 -1.1 -0.1 -0.5 -1.1 -0.1 -0.6 -1.3 -0.1 -0.8 651–700 1.3 29.3 11.8 -19.0 20.2 2.2 -0.5 0.5 0.1 -0.6 0.6 0.1 -0.6 0.4 0.1 701–750 20.4 37.5 30.5 1.9 33.3 25.4 0.1 0.9 0.7 0.3 0.8 0.6 -0.2 0.7 0.4 751–765 36.1 40.0 37.6 26.9 37.8 31.3 0.7 1.0 0.8 0.7 0.9 0.8 0.1 0.6 0.3 ZONE 3 766–800 31.7 57.6 50.5 24.8 56.6 46.6 0.7 1.5 1.2 0.6 1.3 1.1 0.1 1.0 0.6 801–850 25.1 263.8 92.5 -263.8 22.3 -76.5 -6.9 0.6 -2.0 -6.9 0.5 -2.1 -7.6 0.1 -2.8 851–900 56.9 1311.3 583.9 -1129.6 379.0 -394.2 -29.7 10.0 -11.7 -31.4 4.0 -12.7 -30.9 0.0 -14.7 900–950 0.0 1449.6 905.5 -1301.4 1154.2 -144.7 -36.8 30.4 -6.8 -39.0 17.8 -10.4 -40.9 25.4 -12.1 951–970 41.9 1453.7 609.8 -26.1 1337.7 477.9 -0.7 35.2 12.6 -3.1 28.1 7.1 -0.5 29.4 9.6 *SCE and NSM methods are in meters, other methods are in meters/year. In the coastal change analysis for Zone 1, 492 transect lines were created with 50-meter intervals (Trans No: 1-492), and all analyses were evaluated over this line. When the results of the DSAS analysis between 1985 and 2023 are examined, it is determined that there is mostly coastal accretion along the transect line 0-100 and intense coastal erosion along the remaining transect line. This situation constitutes an important factor that threatens Akyatan Lagoon, which is evaluated within the scope of the Ramsar Convention, is the lagoon with the largest area in the Çukurova delta and is one of the important migration points for birds. Akyatan Lagoon is the largest lagoon in Turkiye with an area of 7420 ha. Findings for the NSM method: The maximum coastal advance determined on the 51–100 transect line was 1382.39 m, and the minimum coastal erosion determined on the 351–400 transect line was 76.43 m. Similarly, in the results of the SCE method, the maximum coastal advance determined on the 51–100 transect line was determined to be 1430.63 m for this region. Among the other methods evaluated proportionally, maximum erosion was determined as 2.01 m/yr (transect line 351–400) and maximum accretion as 36.38 m/yr (transect line 51–100) for the EPR method. Similar results were calculated using the LRR method. The maximum erosion and accumulation rates were − 1.80 m/yr (transect line 351–400) and 9.00 m/yr (transect line 51–100), respectively. For the WLR method using the weighted ratio method, the maximum erosion rate was − 2.16 m/yr (transect line 301–350), and the maximum accumulation rate was 14.31 m/yr (transect line 51–100). In general, moderate erosion, stable, and high deposition rates were observed in Zone 1. The reason for the high accretion rate is that the water channel in the region has dried up over time and the shoreline has moved out. A visible drought was observed in this accumulation zone. A total of 273 transect lines were established covering transect numbers from 493 to 765 in Zone 2. When the findings are evaluated in general for all methods, low erosion, stable, and low deposition zones are observed along the coast. When the results of DSAS analysis are analyzed, according to EPR and LRR methods, maximum erosion occurred at transect lines 493–550 and was determined as -2.39 m/yr and − 2.45 m/yr, respectively. The maximum accumulation occurred on transect lines 751–765 with 0.99 m/yr and 0.89 m/yr. The amount of deposition and erosion in the region is quite low. WLR method results also support this situation. The maximum erosion rate was − 3.41 m/yr and the maximum coastal accretion rate was 0.71 m/yr. According to the findings of the NSM method, the maximum amount of erosion experienced in the region is 90.99 m (transect line 493–550) and the maximum accumulation is 37.81 m (transect line 751–765). According to the results of the SCE method, the maximum erosion is 94.82 m, which supports the NSM result. Tuzla Lake or Lagoon is separated from the Mediterranean Sea by a tombolo formed by a narrow and low dune accumulated on the shore, and the water inlet and outlet to the lagoon is provided by a narrow channel (Dural and Göksu, 2004:361). In this respect, it has been determined that there is a coastal change for zone 2 that will not affect the coastal zone and the lagoon near it much. In the coastal change analysis for the Seyhan delta mouth, which is defined as Zone 3, 205 transect lines were created (Trans No: 766–970) and all analyses performed for this region were evaluated over this line. According to the DSAS analysis results, it was determined that the most change was experienced in this region. When the findings are analyzed, the greatest change occurred in the transect line between 900–970, which coincides with the mouth of the delta. According to the results of the NSM method, the maximum accumulation was 1337.72 m and the maximum erosion amount was 1301.4 m. SCE method results also show that the maximum erosion was 1453.65 m. In the Seyhan Delta mouth, the maximum change determined according to the DSAS analysis results is due to the erosion process under the influence of various geological, hydrological, and anthropogenic factors. Water flow, natural erosion, tidal effects, and human impact play a decisive role in the erosion and erosion of the delta mouths in the region. According to the LRR method, the maximum erosion rate was calculated as -39.0 m/yr and the maximum deposition rate as 28.06 m/yr. The severity of erosion and deposition is quite severe here. The results of the EPR method also support the results of the other methods, with a maximum erosion rate of -36.8 m/yr and a maximum deposition rate of 35.20 m/yr. Finally, according to the findings obtained from the WLR method, which is a weighted ratio method, the maximum erosion rate is -40.9 m/yr and the maximum deposition rate is 29.40 m/yr. 3.2 Min-Max normalization results The deposition and erosion values obtained by the different statistical proportioning techniques used in this study, such as EPR, LRR, NSM, SCE, and WLR, are in meters or meters/year. Min-max normalization was applied to statistically compare the results obtained from these techniques. In this way, features measured in different units or varying in different ranges are made comparable because they will be expressed on the same scale, thanks to this normalization technique. Thanks to Min-Max normalization, the values obtained from each technique will be in the range (0, 1). In this way, the values in different units are on the same scale, allowing more accurate comparisons to be made between these values. In this study, Min-Max normalization was applied to the deposition and erosion values obtained from these techniques for three regions created in the Seyhan basin before method-based comparative statistical analysis was performed. Descriptive statistics of min-max normalization results for these 3 regions are given in Table 3 . Table 3 Descriptive statistics of min-max normalization results AREA Method N Min. Max. Mean Std. Deviation Std. Error 95% Confi. Int. for Mean Lower Bound Upper Bound ZONE 1 SCE 492 0.000 1.000 0.311 0.322 0.023 0.267 0.356 NSM 492 0.000 1.000 0.459 0.184 0.013 0.433 0.484 EPR 492 0.000 1.000 0.464 0.192 0.013 0.437 0.490 LRR 492 0.000 1.000 0.504 0.180 0.013 0.479 0.529 WLR 492 0.000 1.000 0.494 0.191 0.013 0.468 0.521 ZONE 2 SCE 273 0.000 1.000 0.064 0.175 0.008 0.048 0.079 NSM 273 0.000 1.000 0.071 0.178 0.008 0.055 0.087 EPR 273 0.000 1.000 0.071 0.178 0.008 0.055 0.087 LRR 273 0.000 1.000 0.124 0.160 0.007 0.110 0.138 WLR 273 0.000 1.000 0.117 0.171 0.008 0.102 0.132 ZONE 3 SCE 205 0.000 1.000 0.409 0.252 0.015 0.379 0.439 NSM 205 0.000 1.000 0.538 0.301 0.018 0.502 0.574 EPR 205 0.000 1.000 0.538 0.302 0.018 0.502 0.574 LRR 205 0.000 1.000 0.555 0.309 0.019 0.518 0.592 WLR 205 0.000 1.000 0.646 0.243 0.015 0.617 0.675 3.3 Accuracy performance analysis of the Kalman Filtering method In order to evaluate the statistical accuracy of the Kalman filter method used in the future prediction of the coastline, a performance analysis was also performed in the study. In the performance analysis, min-max normalization was performed first to make a method-based comparison. In this context, statistical values were obtained by comparing the automatically extracted coastlines of the study area for the years 1985-1990-1995-1995-2000-2000-2005-2010-2015-2020 and 2023 with the estimated coastlines obtained by the Kalman filtering technique on the same date. In the method, coastal data including at least four different time periods should be used in coastline estimation. In the study, 10- and 20-year forecasts were performed in two different scenarios. In the first stage of the accuracy assessment process, 10-year coastline forecasts for four different years were made for the study area. These are respectively: 2005 coastline prediction using 1985–1995 coastline data; 2010 coastline prediction from 1985–2000; 2015 coastline prediction from 1985–2005; and 2020 coastline prediction from 1985–2010. The 2005, 2010, 2015, and 2020 predicted shoreline analysis results were compared with the existing automatically extracted shorelines. The 10-year predicted coastlines for the study area were processed into the model, and the descriptive statistical values for each region defined in the Seyhan basin coastline according to the EPR, LRR, NSM, and WLR analysis results are given in Table 4 below. Figure 7 shows the graphs of RMSE values obtained as a result of the comparisons made for these methods according to different years. Table 4 Descriptive statistical values of 10-year projected shorelines Area Method 2005 2010 2015 2020 Min. Max. Min. Max. Min. Max. Min. Max. Zone 1 SCE -0.05 0.03 -0.04 0.05 -0.04 0.04 -0.03 0.03 NSM -0.04 0.04 -0.05 0.12 -0.04 0.06 -0.03 0.02 EPR -0.1 0.05 -0.13 0.1 -0.05 0.18 -0.05 0.06 LRR -0.11 0.03 -0.12 0.11 -0.14 0.14 -0.06 0.09 WLR -0.09 0.02 -0.04 0.18 -0.04 0.08 -0.06 0.06 Zone 2 SCE -0.02 0.05 -0.02 0.04 -0.03 0.04 -0.02 0.03 NSM -0.01 0.07 -0.04 0.03 -0.02 0.05 -0.01 0.04 EPR -0.03 0.05 -0.08 0.3 -0.02 0.05 -0.03 0.04 LRR -0.33 0.42 -0.03 0.16 -0.95 0.28 -0.01 0.09 WLR -0.17 0.13 -0.12 0.28 -0.1 0.27 -0.17 0.21 Zone 3 SCE -0.25 0.45 -0.44 0.3 -0.36 0.18 -0.26 0.17 NSM -0.37 0.13 -0.15 0.34 -0.13 0.27 -0.11 0.18 EPR -0.36 0.41 -0.25 0.3 -0.2 0.26 -0.11 0.18 LRR -0.2 0.58 -0.21 0.32 -0.27 0.29 -0.07 0.12 WLR -0.34 0.65 -0.4 0.42 -0.36 0.39 -0.21 0.3 When the above results are evaluated in general, SCE, NSM, and EPR methods show higher performance in Zone 1 compared to other methods. Especially SCE and NSM seem to be close to each other until 2020. The LRR method continues to decrease its performance until 2015, but shows an increase in performance in the following years. The WLR method generally performs better in Zone 1 than in other zones. In this zone, it shows a decline from 2015 to 2020. In Zone 2, the SCE, NSM, and EPR methods show generally high performance from 2005 to 2020. The performance of these methods remains fairly stable and at a high level. While the LRR method performs poorly until 2010, its performance improves in the following years. The WLR method, which has the lowest performance among these methods, continues to increase its performance until 2020. In Zone 3, the performance of all methods decreases compared to Zone 2 and Zone 1. The SCE, NSM, and EPR methods show low performance from 2005 to 2020. However, they have higher values compared to Zone 2. The LRR method continues to improve its performance until 2020. The WLR method generally underperforms, but its performance improves in 2020. In conclusion, according to the given data, there are different trends in the performances obtained with different measurement methods in Zone 1, Zone 2, and Zone 3 over the years. More detailed analyses can be made of the causes and consequences of these trends. These interpretations may vary depending on the characteristics, data sets, and use cases of each region. It can play an important role in the correct selection and analysis of measurement methods and regions. In addition, 20-year coastline forecasts were made for two different years in the study area. These are, respectively, 2015 coastline estimation using 1985–1995 coastline data and 2020 coastline estimation using 1985–2000 coastline data. The 2015 and 2020 predicted shoreline analysis results were compared with the existing automatically extracted shorelines. The 20-year predicted shorelines for the study area were processed into the model, and the descriptive statistical values for each region defined in the Seyhan basin coastline according to the EPR, LRR, NSM, and WLR analysis results are given in Table 5 below. Figure 8 shows the graphs of the RMSE values obtained as a result of the comparisons made for these methods according to different years. Table 5 Descriptive statistical values of 20-year projected shorelines Area Method 2015 2020 Min. Max. Min. Max. Zone 1 SCE -0.05 0.03 -0.08 0.05 NSM -0.05 0.28 -0.04 0.07 EPR -0.09 0.28 -0.27 0.37 LRR -0.06 0.19 -0.23 0.09 WLR -0.04 0.24 -0.08 0.13 Zone 2 SCE -0.03 0.03 -0.05 0.05 NSM -0.10 0.29 -0.04 0.08 EPR -0.19 0.44 -0.04 0.08 LRR -0.15 0.10 -0.37 0.31 WLR -0.06 0.17 -0.13 0.20 Zone 3 SCE -0.52 0.46 -0.56 0.26 NSM -0.31 0.30 -0.18 0.43 EPR -0.33 0.29 -0.18 0.42 LRR -0.30 0.41 -0.14 0.43 WLR -0.41 0.42 -0.31 0.54 In the tables and graphs above, in Zone 2 for 2015, the highest values are observed in the coastal boundary change performance with the SCE method, while significant values are obtained in the determinations made with the NSM and EPR methods. Other methods have higher performance. In Zone 3, the coastal boundary change performance with the SCE method decreased the most. It varies with other methods. In 2020, in Zone 2, the performances with the SCE method showed the highest performance compared to other methods. In other methods, the results of the NSM and EPR methods are closer to each other. In Zone 3 and Zone 1, there is a general downward trend in performance. In general, in Zone 2 and Zone 3, the results in 2020 are better than in 2015, while in Zone 1, SCE and EPR methods show a reverse performance decrease. Especially in Zone 3, there is a general downward trend in performances, but this trend varies across methods. In addition, the Kalman filter model was analyzed to determine the performance of the prediction time in 10-year and 20-year future forecasts for coastline delineation for the years 2033 and 2043. As an example, the method-based performances of 10-year and 20-year future forecasts for 2015 are given in Fig. 9 . In the figure above, when comparing the 10-year and 20-year performances of different methods in different zones, important findings have been reached. In Zone 1, the results with SCE, NSM, and EPR methods show that 10-year performances are generally better than 20-year performances. However, a different trend was observed with the LRR and WLR methods, with 10-year performances generally better than 20-year performances. In Zone 2, no significant change was observed between 10 and 20-year performances for the measurements with the SCE method. Results with NSM and EPR methods show that 10-year performances are generally better than 20-year performances. However, different trends are observed for the LRR and WLR methods. In Zone 3, for all measurement methods, 10-year performances are generally better than 20-year performances. This suggests that the long-term performance of the methods in Zone 3 is worse. These results provide important clues for understanding the long-term performance of a given method and for optimizing measurement processes. In general, when the RMSE values obtained from 10-year and 20-year forecasts are analyzed, it is seen that less time in the future forecast affects the performance in determining coastal boundary changes. 3.4 Coastal future forecast for 2033 and 2043 Based on the results of the obtained shorelines and rates of change, the Kalman filter model was used to predict the future shoreline for the years 2033–2043. In this way, it is aimed at providing a scientific basis for reducing environmental risks in the region, protecting natural resources, and promoting sustainable development in coastal areas. The future coastline forecast model was simulated using the beta forecast tool in the DSAS tool. In Fig. 10 , in addition to the 1985–2023 shoreline data, the estimated shoreline data for the years 2033 and 2043 are presented in vector form. It also takes into account possible uncertainties in the simulated shorelines. In estimation methods such as the Kalman filter, uncertainty may be due to the model used or measurement uncertainties. The model used may not have complete information about the real system or may not fully represent all effects in the system. In this case, uncertainties in the model can affect the accuracy of future predictions. During the actual measurement process, there may be uncertainties in data such as shorelines and rates of change. Measurement errors can be related to the accuracy or precision of the measuring instruments. These uncertainties should be accounted for in estimation methods such as the Kalman filter and taken into account to assess the reliability of the estimates. Uncertainty can be expressed in terms of the confidence intervals of the predictions or a given confidence level. In this way, more information about how reliable the forecasts are can be provided and help decision-makers make the right decisions. In the study, model analysis was performed for all methods to determine possible coastal changes for the rate of change and ± uncertainty values at a 95% confidence interval. Future coastline predictions for the years 2033–2043 using the Kalman filter model are given in Table 6 below. The results of the coastal change analysis performed with the estimated coastlines are explained separately for the years 2033 and 2043. According to the DSAS report generated as a result of the analysis, the coastal boundary changes estimated for 2033 show various trends in the coastal dynamics in the Seyhan Delta region. The SCE shows an average change of 189.05 meters over a total of 485 transects. The largest change was recorded in Zone 3 (transect 479) with 1488.62 meters, and the smallest change was recorded in Zone 1 (transect 1) with 1.57 meters. The NSM shows a decline in 73.4% of the total number of transects (with an average change of -16.41 meters) and a growth of 26.6% (with an average change of 225.16 meters). The largest regression was observed in Zone 3 (transect 465') with − 1476.17 meters. The EPR shows an average regression of -0.69 meters per year. While a statistically significant regression was detected in 73.4% of the transects experiencing erosion, the average erosion value of these transects was calculated as -2.69 meters/year. In 26.6% of the transects that experienced accumulation, an average growth of 4.81 meters/year was observed. LRR shows an average regression of -1.27 meters/year. A statistically significant regression was detected in 75.26% of the transects experiencing erosion, and the average erosion value of these transects was calculated as -2.68 meters/year. In 24.74% of the transects that experienced accumulation, an average growth of 3.03 meters/year was observed. WLR shows an average regression of -1.98 meters/year. A statistically significant regression was detected in 79.38% of the transects experiencing erosion, and the average erosion value of these transects was calculated as -3.05 meters/year. In 20.62% of the transects experiencing accretion, an average growth of 2.12 meters/year was observed. These data provide a general summary of the expected shoreline changes in 2033. In a period dominated by erosion, some areas experienced significant growth. However, there is a certain margin of uncertainty for each type of rate, and these results provide a complex picture of the coastal dynamics in the Seyhan Delta region, with shoreline changes estimated for the year 2033. For the year 2043, the changes in the analyzed shorelines generally reflect the erosion trend. However, local accumulation is observed in some specific areas. For example, according to SCE data, the average change along the delta is 155.07 meters, but the large difference between the minimum and maximum values is striking. In particular, the maximum distance of up to 1484.03 meters indicates a marked retreat in some parts of the coast. The NSM data show a negative movement in general (81.86%) and a positive movement in particular at 88 transects (18.14%). This positive movement may indicate that sediment deposition in certain areas of the Seyhan Delta has caused localized widening of the shorelines. On the other hand, when we focus on erosion rates, we see that the average erosion rates calculated by the EPR, LRR, and WLR methods are − 2.88, -3.05, and − 2.92 meters/year. These figures indicate a rapid retreat along the coast in general. It is particularly noteworthy that the maximum erosion rate of transect 441 is -49.47 meters/year, highlighting the serious erosion problem in some areas along the coast. As a result, changes in the Seyhan Delta coastal margin indicate erosion as a general trend, while local accumulation is observed in certain places. Especially for Akyatan and Tuzla lagoons, which are considered within the scope of the Ramsar site, located next to Zone 1 and Zone 2, and in which biodiversity is high, it is predicted that there will be more erosion-prone areas that will lead to significant losses. When the rates of coastal change according to the forecasts made between 2033 and 2043 are compared, it is seen that the rate of coastal change decreases in 2043, but coastal erosion becomes more pronounced. Evaluation of factors such as average coastal change, net coastal movement, endpoint velocity, linear regression velocity, and weighted linear regression velocity indicates that coastal erosion will increase in 2043, which can be attributed to climate change and environmental impacts. These findings provide important insight into the management and protection of delta coasts and can help develop strategies to cope with the impacts of coastal erosion. 3.5 ANOVA Test Results with Kruskal-Wallis H Test Normal distribution and homogeneity of variance tests were performed to determine whether the data sets of the methods to be compared for coastal boundary change are parametric. In this study, the choice of parametric or non-parametric methods was made by considering the potentially misleading results of normal distribution and homogeneity. For this purpose, the non-parametric Kruskal-Wallis H test and the parametric ANOVA test were applied. These analyses were applied to obtain reliable and comprehensive results, depending on the distributional characteristics of the data set. By comparing the results of both analysis methods, it was determined whether there is a statistically significant difference between the temporal and geometric changes in the coastline in the Seyhan basin. In addition, subgroups were formed between different proportioning techniques according to the accumulation and erosion rates defined as dependent variables with SPSS software. These subgroups were created to determine whether the method-based results show the same or different characteristics in the coastal boundary changes over the years within the study area. Table 7 shows the results of the homogeneity of variance test and the normal distribution test. Table 6 Future coastline predictions for the years 2033-2043 Table 7 Homogeneity of variance and normal distribution test results Tests of Normality AREA Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. ZONE 1 0.113 1025 0.000 0.957 1025 0.000 ZONE 2 0.306 2460 0.000 0.412 2460 0.000 ZONE 3 0.088 1365 0.000 0.943 1365 0.000 a . Lilliefors Significance Correction Test of Homogeneity of Variances AREA Levene Statistic df1 df2 Sig. ZONE 1 41.422 4 1020 0.000 ZONE 2 0.078 4 2455 0.989 ZONE 3 14.253 4 1360 0.000 When the results obtained from the homogeneity of variance and normal distribution tests for the accumulation and erosion rates in the 3 main regions in the Seyhan basin are analyzed, it is determined that the coastal change rates in all regions are not normally distributed. Since the values in the Sig. column of this table are less than 0.05, it is determined that the coastal change rates in all regions are not normally distributed. Except for Zone 2, the variances in accretion and erosion rates in the other zones are not homogeneous. In this study, statistical comparisons should be made according to the Kruskal-Wallis H test according to normal distribution and variance homogeneity. However, considering the potentially misleading results of normal distribution and homogeneity in data analysis, both the Kruskal-Wallis H Test and ANOVA analysis were performed in this study, and the results are given in Tables 8 and 9 below. Table 8 Kruskal-Wallis H Test results Test Statistics a,b ZONE 1 ZONE 2 ZONE 3 Chi-Square 108.667 785.227 89.217 df 4 4 4 Asymp. Sig. 0.000 0.000 0.000 a. Kruskal Wallis Test b. Grouping Variable: method Table 9 Analysis of variance (ANOVA) test results ANOVA AREA Sum of Squares df Mean Square F Sig. ZONE 1 Between Groups 4.98 4 1.245 25.552 0.000 Within Groups 49.701 1020 0.049 Total 54.681 1024 ZONE 2 Between Groups 1.618 4 0.404 13.581 0.000 Within Groups 73.099 2455 0.03 Total 74.717 2459 ZONE 3 Between Groups 7.816 4 1.954 24.418 0.000 Within Groups 108.833 1360 0.08 Total 116.649 1364 When the Kruskal-Wallis H Test or ANOVA results and the averages of the groups are evaluated together, it is seen that the value in the Sig. column in the tables is less than 0.05. These results indicate that the results obtained from the techniques used to determine the temporal and geometric changes in the coastline in the Seyhan basin are different; that is, there is a statistically significant difference. However, the results of the Kruskal-Wallis H test or ANOVA test do not contain information about which groups these differences are between. For this reason, multiple comparisons (post-hoc tests) were made to determine which techniques the difference was between. In the following tables (Tables 10 – 12 ), pairwise comparisons of the techniques with or without a statistically significant difference between them are given according to Tukey HSD and Tamhane's T2 test. Table 10 Pairwise comparisons by method for Zone 1 (Tukey HSD and Tamhane's T2) Multiple Comparisons ZONE 1 Tukey HSD Tamhane’s T2 (I) method (J) method Mean Difference (I-J) Sig. Mean Difference (I-J) Sig. SCE NSM -0.147 0.000 -0.147 0.000 EPR -0.152 0.000 -0.152 0.000 LRR -0.193 0.000 -0.193 0.000 WLR -0.183 0.000 -0.183 0.000 NSM SCE 0.147 0.000 0.147 0.000 EPR -0.005 0.999 -0.005 1.000 LRR -0.045 0.229 -0.045 0.115 WLR -0.036 0.478 -0.036 0.435 EPR SCE 0.152 0.000 0.152 0.000 NSM 0.005 0.999 0.005 1.000 LRR -0.040 0.345 -0.040 0.254 WLR -0.031 0.627 -0.031 0.677 LRR SCE 0.193 0.000 0.193 0.000 NSM 0.045 0.229 0.045 0.115 EPR 0.040 0.345 0.040 0.254 WLR 0.010 0.992 0.010 1.000 WLR SCE 0.183 0.000 0.183 0.000 NSM 0.036 0.478 0.036 0.435 EPR 0.031 0.627 0.031 0.677 LRR -0.010 0.992 -0.010 1.000 The mean difference is significant at the 0.05 level. Table 11 Pairwise comparisons by method for Zone 2 (Tukey HSD and Tamhane's T2) Multiple Comparisons ZONE 2 Tukey HSD Tamhane’s T2 (I) method (J) method Mean Difference (I-J) Sig. Mean Difference (I-J) Sig. SCE NSM -0.007 0.966 -0.007 0.999 EPR -0.007 0.966 -0.007 0.999 LRR -0.060 0.000 -0.060 0.000 WLR -0.053 0.000 -0.053 0.000 NSM SCE 0.007 0.966 0.007 0.999 EPR 0.000 1.000 0.000 1.000 LRR -0.053 0.000 -0.053 0.000 WLR -0.046 0.000 -0.046 0.000 EPR SCE 0.007 0.966 0.007 0.999 NSM 0.000 1.000 0.000 1.000 LRR -0.053 0.000 -0.053 0.000 WLR -0.046 0.000 -0.046 0.000 LRR SCE 0.060 0.000 0.060 0.000 NSM 0.053 0.000 0.053 0.000 EPR 0.053 0.000 0.053 0.000 WLR 0.007 0.967 0.007 0.999 WLR SCE 0.053 0.000 0.053 0.000 NSM 0.046 0.000 0.046 0.000 EPR 0.046 0.000 0.046 0.000 LRR -0.007 0.967 -0.007 0.999 The mean difference is significant at the 0.05 level. Table 12 Pairwise comparisons by method for Zone 3 (Tukey HSD and Tamhane's T2) Multiple Comparisons ZONE 3 Tukey HSD Tamhane’s T2 (I) method (J) method Mean Difference (I-J) Sig. Mean Difference (I-J) Sig. SCE NSM -0.129 0.000 -0.129 0.000 EPR -0.129 0.000 -0.129 0.000 LRR -0.146 0.000 -0.146 0.000 WLR -0.237 0.000 -0.237 0.000 NSM SCE 0.129 0.000 0.129 0.000 EPR 0.000 1.000 0.000 1.000 LRR -0.017 0.958 -0.017 0.999 WLR -0.109 0.000 -0.109 0.000 EPR SCE 0.129 0.000 0.129 0.000 NSM 0.000 1.000 0.000 1.000 LRR -0.017 0.959 -0.017 0.999 WLR -0.108 0.000 -0.108 0.000 LRR SCE 0.146 0.000 0.146 0.000 NSM 0.017 0.958 0.017 0.999 EPR 0.017 0.959 0.017 0.999 WLR -0.092 0.001 -0.092 0.001 WLR SCE 0.237 0.000 0.237 0.000 NSM 0.109 0.000 0.109 0.000 EPR 0.108 0.000 0.108 0.000 LRR 0.092 0.001 0.092 0.001 The mean difference is significant at the 0.05 level. As an example, when Table 11 , which shows the multiple comparisons in Zone 2, is examined, it is statistically determined that the SCE technique shows similar characteristics with NSM and EFR and different characteristics with LRR and WLR in the multiple comparisons made between statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR with the DSAS tool to determine the temporal and geometric changes in the coastline in the Seyhan basin. The NSM technique shows similar characteristics with SCE and EPR but different characteristics with LRR and WLR. The EPR technique shows similar characteristics with SCE and NSM and different characteristics with LRR and WLR. The LRR method shows different characteristics from other methods, except WLR. Finally, it is statistically determined that the WLR method is similar to LRR and different from other methods. In addition, according to the different techniques by which the accumulation and erosion rates, which are defined as dependent variables, were calculated, the subgroups specified in Table 13 were formed for the three regions forming the coastline in the Seyhan basin. Table 13 Subgroups between different techniques used in determining coastal changes according to three different regions Tukey HSDa ( Subset for alpha = 0.05) Method ZONE 1 ZONE 2 ZONE 3 1 2 1 2 1 2 3 SCE 0.311 0.064 0.409 NSM 0.459 0.071 0.538 EPR 0.464 0.071 0.538 WLR 0.494 0.117 0.555 LRR 0.504 0.124 0.646 Sig. 1.000 0.229 0.966 0.967 1.000 0.958 1.000 Means for groups in homogeneous subsets are displayed a. Uses Harmonic Mean Sample Size = (ZONE 1 = 492, ZONE 2: 273, ZONE 3: 205) To determine whether statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR have similar characteristics to determine the temporal and geometric changes in three different coastlines in the Seyhan basin, subgroups were formed. In Zone 1, two subclasses were formed. In Zone 2, SCE, NSM, and EPR have similar characteristics, while WLR and LRR techniques differ from other techniques. In Zone 3, three different subclasses were formed. SCE and LRR formed a subclass on their own. These methods produce results that differ from each other. NSM, EPR, and WLR show similar characteristics. While SCE is different from the other methods, the results of the other methods are similar to each other. In general, when the subgroups obtained from these 3 regions are evaluated, similar results are obtained in determining coastal changes with NSM and EPR techniques. The results of other techniques have different or similar characteristics, varying according to the region. The SCE method differs from other methods in Zone 1 and Zone 3. The LRR method is similar to the WLR method in Zone 2 and differentiates from other methods in Zone 3. In Zone 1, it is similar to the other methods except for the SCE method. 4. Conclusion Coastal areas are sensitive regions rich in natural resource potential and biodiversity, offering important economic opportunities for society but also under development pressure. Coastal areas, where settlements and civilizations have historically developed and where cultural and economic interactions have been intense, are today facing various use demands along with coastalization trends. Urban uses, industrial facilities, energy terminals, shipyards, touristic facilities, fishing, and other activities increase competition in coastal areas. However, meeting these demands in an unplanned manner leads to incompatible use patterns, the destruction of natural resources, and the deterioration of the ecological balance. These unplanned development trends cause social, economic, environmental, and spatial problems. Environmental problems such as degradation of ecosystems in coastal areas, loss of balance between marine and terrestrial ecosystems, erosion, and sea level rise arise. At the same time, conflicts between communities living in coastal areas are increasing in terms of site selection, resource utilization, and urban planning. In this context, it is of great importance to adopt a planned and multi-stakeholder approach in line with sustainable development goals in coastal areas. Developing an integrated planning and management strategy for the protection of coastal areas, sustainable use of natural resources, restoration of ecosystems, and economic well-being of communities will contribute to solving problems in coastal areas. This study is designed to examine the changes in the coastline of the Seyhan Delta between 1985 and 2023, to understand the causes and effects of these changes, to analyze them from a scientific perspective, to determine the positions of the coastline in the next 10 and 20 years, to compare statistical results by making predictive modeling with the Kalman filtering technique, and to investigate the accuracy of this model. The coastal change rates determined in the study were analyzed by different parameters (EPR, LRR, WLR, NSM, and SCE) at 95% confidence intervals. According to the SCE model results, the maximum annual accumulation rate is 1453.65 m2, the NSM model results show that the maximum annual accumulation rate is 1382.39 m2, and the maximum erosion rate is -1301.41 m2. EPR model results show that the maximum annual accumulation rate is 36.38 m/yr and the maximum erosion rate is -36.85 m/yr; WLR model results show that the maximum annual accumulation rate is 29.40 m/yr and the maximum erosion rate is -40.87 m/yr; and finally, LRR model results show that the maximum annual accumulation rate is 28.06 m/yr and the maximum erosion rate is -39.00 m/yr. Different statistical analysis methods (Kruskal-Wallis H test, ANOVA, multiple comparison analysis) were used to evaluate the statistical significance of the results. To assess the statistical significance of the results, a focused approach was adopted to select appropriate analysis methods. The data set for each technique was examined in detail at the 95% significance level to determine whether it was normally distributed and homogeneous in variance. In the analyses performed in three different regions, Zone 1, Zone 2, and Zone 3, it was observed that SCE, NSM, and EPR techniques showed similar characteristics. The WLR and LRR techniques gave different results. In Zone 3, SCE and LRR formed their subclasses, while in Zone 1, methods other than SCE gave similar results. In general, NSM and EPR techniques provided similar results in determining coastal changes, but other techniques differed by region. In the study, the Kalman filter model used in the future prediction of the coastline was used to determine the coastline locations for the years 2033 and 2043 under two different scenarios. In addition, performance analysis was also performed in the study to evaluate the statistical accuracy of the model. In the study, important findings were obtained while comparing the 10-year and 20-year performances of different methods in different regions. When the RMSE values of the 10-year and 20-year forecasts are analyzed, it is seen that the longer prediction period in future forecasts negatively affects the performance in determining coastal boundary changes. This suggests that shorter-term forecasts tend to yield more reliable results. It is thought that shorter-term forecasts can more accurately predict coastal boundary changes and can be a more effective tool in managing these changes. When all the analyses performed in the study are examined, it is determined that the rate of coastal change along the coast is gradually increasing, and this acceleration trend indicates erosion. According to the results of the DSAS analysis, it was determined that the most change was experienced in the Seyhan delta river mouth, defined as Zone 3. Erosion of delta mouths is a result of various geological, hydrological, and anthropogenic factors. Geologic factors here can occur under the influence of different geologic processes. In the Seyhan Delta, processes such as water flow, natural erosion, and tidal effects have been effective. Apart from this, various water movements, flow rate and intensity in the river, and sea level rise accelerate the erosion process. In addition, human impact, which is referred to as an anthropogenic factor, is also an important factor in the erosion of delta mouths. It has been determined that various activities, such as coastal development projects, the presence of sewage discharges, and agricultural and industrial wastes, trigger erosion in the region and affect natural processes. When the results of the study and regional characteristics are analyzed, it is necessary to take protective measures for the intervention of natural factors as well as artificial factors. Effective management plans should be developed, and existing strategies should be strengthened to prevent the destruction of the ecosystem and the restoration of vegetation along the coastline. Continuous nourishment of the areas used as beaches in the region will be effective in preventing coastal erosion. All academic research shows that monitoring and understanding the changes in coastal zones is critical for sustainable coastal management. These changes can be associated with factors such as erosion, coastalization, habitat loss, and other environmental impacts. Advanced remote sensing technologies provide important tools to detect and analyze these changes. In the future, these technologies are expected to develop further and become more widespread. This will make it possible to monitor changes in coastal zones more precisely, manage natural resources more effectively, and respond more quickly to environmental problems. Furthermore, future coastal change trends can be predicted through the use of remote sensing technologies. These predictions can shape the future use and protection of coastal areas, playing an important role in planning processes. Therefore, it is necessary to promote the use of remote sensing technologies in coastal areas and to evaluate these data in an integrated manner in line with sustainable development goals. This approach will undoubtedly play an important role in the future of coastal regions for the preservation of economic prosperity, sustainable use of natural resources, and conservation of biodiversity. Declarations Conflict of interest The authors declare that there is no conflicts of interest. Acknowledgments The author is thankful to the U.S. Geological Survey for providing facilities to carry out this study by making the data available in open access. Author contributions Münevver Gizem Gümüş, conceptualized and designed the study, collected and analyzed the data, interpreted the results, and wrote the manuscript. Funding If accepted, this article will be funded as open access by the Scientific and Technological Research Council of Turkey (TÜBİTAK). Data availability Datasets generated during the current study are available from the corresponding author on reasonable request. References Abd-Elhamid HF, Zeleňáková M, Barańczuk J, Gergelova MB, Mahdy M (2023) Historical trend analysis and forecasting of shoreline change at the Nile Delta using RS data and GIS with the DSAS tool. Remote Sens 15(7):1737. https://doi.org/10.3390/rs15071737 Acciarri A, Bisci C, Cantalamessa G, Di Pancrazio G (2016) Anthropogenic influence on recent evolution of shorelines between the Conero Mt. and the Tronto R. mouth (southern Marche, Central Italy). 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Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2024 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviews received at journal 12 Jun, 2024 Reviewers agreed at journal 01 Jun, 2024 Reviewers agreed at journal 01 Jun, 2024 Reviewers agreed at journal 31 May, 2024 Reviewers agreed at journal 31 May, 2024 Reviewers invited by journal 31 May, 2024 Editor assigned by journal 31 May, 2024 Submission checks completed at journal 23 May, 2024 First submitted to journal 13 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4411235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309323910,"identity":"4f723b08-b384-4140-9bc2-6a81daa7ba56","order_by":0,"name":"Münevver Gizem GÜMÜŞ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZhBxQALC+cBwAEQZEK+FcQZcSwIhqw5AtfMQo0Xenf3hoxtnLOz5249f/GxTcSexgb15mwTjj3s4tRgeZkg2zrkhkTjjTE6xdM6ZZ4kNPMfKJBgSinFraWY4Jp3zQSKB4UBOgnRu2+HEBokcM6AW3C4zbGZs/w3UYi9//k3yb0uQFvk3+LXIMzOzMQMdxrjhRvoxaUawLTz4tRgAdQC9IJG48cYbNsueM4eN23jSii0S0vDY0n/84eecY3X2cufTH9/4UXFYtp/98MYbH2zw2HIAzuSBRDobiMCtAWhLA5zJ/gCPulEwCkbBKBjJAAAgVFgBRsMQlgAAAABJRU5ErkJggg==","orcid":"","institution":"Niğde Ömer Halisdemir University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Münevver","middleName":"Gizem","lastName":"GÜMÜŞ","suffix":""}],"badges":[],"createdAt":"2024-05-13 07:02:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4411235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4411235/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12145-024-01445-w","type":"published","date":"2024-08-21T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57953797,"identity":"ef5db5ec-a9c9-474b-86d7-b26996904073","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":675601,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology flow chart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/157219b77a575101a13ccefc.png"},{"id":57955340,"identity":"73e94a2a-0476-4745-96e7-ba22024a1470","added_by":"auto","created_at":"2024-06-07 23:27:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2523804,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/32240cfabb98c5b72220c8ed.png"},{"id":57953800,"identity":"3439f795-ea21-4742-a304-4ad1db6433e6","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":788487,"visible":true,"origin":"","legend":"\u003cp\u003eAutomatic boundary extraction on the GEE platform: a) image composite generation; b) land-water boundary sedimentation; c) automatic threshold determination with the Otsu threshold; d) land-water boundary extraction in vector format\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/164e15a46cfe3092a0d03110.png"},{"id":57954790,"identity":"399e0bc0-90b3-481a-8fc5-bfb7bad9a470","added_by":"auto","created_at":"2024-06-07 23:19:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1241937,"visible":true,"origin":"","legend":"\u003cp\u003eNSM and SCE analysis results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/0e19af8726113141a79b9a1b.png"},{"id":57954788,"identity":"c55f4831-ea42-4a32-bf94-0189ee35531f","added_by":"auto","created_at":"2024-06-07 23:19:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1323814,"visible":true,"origin":"","legend":"\u003cp\u003eWLR and LRR analysis results\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/717e7a4e6d809be9c44c87e3.png"},{"id":57955341,"identity":"22f8395b-368b-4b21-9d20-0ccdde35d63b","added_by":"auto","created_at":"2024-06-07 23:27:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":699165,"visible":true,"origin":"","legend":"\u003cp\u003eEPR analysis results\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/0076a1b09d7a488a7671af1b.png"},{"id":57953804,"identity":"27f1bd73-6efe-4eb6-ab76-9fb8f157724e","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":42452,"visible":true,"origin":"","legend":"\u003cp\u003e10-year projected RMSE values obtained according to different methods\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/e774b549980ff22bad9adf45.png"},{"id":57953805,"identity":"61e222c1-78c4-4c4a-9152-21a7400b9a5f","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":28815,"visible":true,"origin":"","legend":"\u003cp\u003e20-year projected RMSE values obtained according to different methods\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/4f90d29d0a0fa3e317e0500a.png"},{"id":57953803,"identity":"3a7a3cd9-12ce-4dc5-9fbc-0af0605daa8c","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33442,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of RMSE values obtained according to different methods for 10 years and 20 years for 2015\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/d648c4f98ef84cfe842f218e.png"},{"id":57953806,"identity":"b8f0e08e-4ede-4bc6-838e-83b8e0f240ca","added_by":"auto","created_at":"2024-06-07 23:11:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1831795,"visible":true,"origin":"","legend":"\u003cp\u003eVectorized map of the coastline projected for 2033 and 2043\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/864b21f3c0f07103237d5886.png"},{"id":63300072,"identity":"95ef8817-aa8d-47bd-9e4f-1cab18c260ee","added_by":"auto","created_at":"2024-08-26 16:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15996695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4411235/v1/9e58556d-d383-46bf-8472-2fb81ada3cf7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting future scenarios of coastline changes in Turkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoastal zones are of vital importance in many aspects, such as biodiversity conservation, sustainable management of water resources, economic activities, and human settlements (Vernberg and Vernberg \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hinrichsen \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Coasts stand out as dynamic areas constantly evolving through natural processes and human intervention (\u0026Ccedil;olak \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Farris et. al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alesheikh et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schwartz \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Factors such as disaster risks in coastal areas (global climate change, sea level rise, coastal erosion), rapid and uncontrolled construction of coasts, location confusion between different sectors, loss of natural resources in coastal areas, deterioration of archaeological, historical, and cultural values are among the factors that constantly affect coastlines (French \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A detailed study of these changes is important from environmental, economic, and social perspectives. Research into the causes, impacts, and management of coastal change plays an important role in the sustainable use and conservation of coastal areas. Conventional field survey methods, such as this study, are time-consuming, costly, and labor-intensive for a large coastline survey (Masek et. al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kundu and Mandal \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For effective understanding and management of these processes, the use of digital coastal inference methods, UA and GIS technologies offers many advantages (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Skilodimou et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dua et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). UA provides the capacity to remotely monitor and analyze coastal changes. GIS provides a powerful tool to organize, analyze, and visualize complex geographic data. The effective use of these technologies helps us to better understand changes in coastal zones, predict future impacts, and develop sustainable management strategies (Huang et. al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Davidson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The analysis of coastal changes, when integrated with UA and GIS technologies, allows us to make stronger and more scientifically based decisions on issues such as disaster risk reduction, wildlife conservation, and effective management of tourism activities (Kafrawy and Ahmed \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, these technologies play a critical role in the process of continuously monitoring and analyzing coastal changes to improve the quality of life of people living in coastal areas and to secure their economic well-being sustainably.\u003c/p\u003e \u003cp\u003eMany methods and techniques have been developed in the literature to analyze coastal environmental changes. In this context, the study of coastal topographic features and morphological changes is an important first step to understanding coastal dynamics (Karunarathna et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can be accomplished using topographic maps and satellite imagery to assess processes such as coastal erosion, coastal accretion, and sedimentation. The study of hydrological factors such as coastal water movement, tides, and currents is important to understand the movement and interaction of water bodies in coastal areas (Thakur et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Acciarri et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These analyses include parameters such as river flow, water temperature, and salinity (Abd-Elhamid et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study of coastal sedimentary processes and sediment properties is used to understand beach and seafloor changes (Goudie \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miliman \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Brandt \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Williams and Wolman \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Analysis of sediment samples provides information on sediment transport and storage (Tagil and C\u0026uuml;rebal \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are different studies in the literature on digital coastline analysis and future prediction using time series. Kundu and Mandal (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) focused on the detection of shoreline change and future prediction of the Sundarban Delta using a digital shoreline analysis system with the help of multispectral satellite imagery and geographic information systems. Palanisamy et. al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyzed 60 km of shoreline changes on the south coast of Rio de Janeiro from 1986 to 2018 using satellite imagery and statistically determined the rates of change. They also identified projected coastal locations for 2032\u0026ndash;2042, emphasizing that human activities can affect coastal patterns, protective measures can affect erosion and deposition patterns, coastal development activities can disrupt natural processes, and effective coastal management strategies are essential for the protection of ecosystems and communities. Ataol et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in their study, aimed to determine the erosion rates in the Kızılırmak delta river mouth, which is subjected to geomorphological changes due to the large dams built on it, and as a result, it is observed that the dams built near the delta cause an increase in erosion on the coastline. As observed all over the world, external interventions such as structures built in the rivers belonging to deltas in Turkiye continuously threaten delta coastal boundary changes (Ataol et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kuleli et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Especially the Mediterranean basin is considered to be one of the most vulnerable regions affected by climate change (Nicholls and Hoozemans \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lionello et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this context, it is observed that in our country, especially on the Mediterranean delta coasts, change detection studies on coastlines are increasing (Ozaner \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Kazı and Karabulut \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ciritci and T\u0026uuml;rk \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kılar and \u0026Ccedil;i\u0026ccedil;ek \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ataol and Kale \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003em\u0026uuml;s et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, understanding and responding appropriately to changes over time in critical coastal areas such as the Seyhan Basin in Turkiye, the study area, is critical for environmental sustainability and community resilience.\u003c/p\u003e \u003cp\u003eThis paper aims to analyze the geometric changes in the coastline of the Seyhan Basin in Turkiye over time using DSAS V.5.0 (2021) from a scientific perspective and to make future predictions. The paper consists of five main phases. In the first stage, using the GEE (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) platform, automatic extraction of the coastline in vector format using Landsat satellite imagery between 1985 and 2023 (approximately 40 years) will be determined using the Automated Water Extraction Index (AWEI). In the second stage, all data will be exported and processed in the ArcGIS environment, and coastal change rates will be calculated using different statistical methods with the DSAS tool. In the third stage, 10- and 20-year performance analyses will be performed to evaluate the statistical accuracy of the Kalman filter method used in the future prediction of the coastline. In the fourth stage, coastline forecasts will be made for the years 2033\u0026ndash;2043 using the Kalman filter model based on the analyses obtained. In the fifth stage, the Kruskal-Wallis H test and ANOVA test will be used to determine whether there is a statistically significant difference between the results obtained using different methods of coastal change. In this way, it is aimed at establishing a scientific basis by statistically analyzing coastal boundary changes depending on method and time to reduce environmental risks in the region, protect natural resources, and support sustainable development in coastal areas.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eIn this paper, the computational power of DSAS and GEE was used to analyze the temporal change along the coastline. By downloading the annual median composites of satellite imagery through the GEE platform, it is aimed at performing automatic coastline extraction. The shoreline extraction was analyzed using the Landsat collection of medium spatial resolution and multi-temporal satellite imagery from the United States Geological Survey (USGS) Earth Explorer archive (USGS \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Landsat imagery plays an important role in coastline inference because this type of remote sensing data allows for a detailed analysis of coastal dynamics by delineating the boundaries between water and land (Marfai et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Teodoro \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The satellite imagery collections and their technical specifications used for coastal change analysis over 40 years between 1985 and 2023 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The technical workflow diagram of the study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSatellite image collections and technical specifications used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollection name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollection Snippet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScene size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePath/Row\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1985/1990/1995/2000/2005/2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSGS Landsat 5 Level 2, Collection 2, Tier 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.ImageCollection\u003c/p\u003e \u003cp\u003e(\"LANDSAT/LT05/C02/T1_L2\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 km x 183km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e175/034\u0026ndash;175/035\u0026ndash;176/034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015/2020/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSGS Landsat 8 Level 2, Collection 2, Tier 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eee.ImageCollection\u003c/p\u003e \u003cp\u003e(\"LANDSAT/LC08/C02/T1_L2\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 km x 183km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e175/034\u0026ndash;175/035\u0026ndash;176/034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eIn this paper, the Seyhan Basin in Turkiye is used as the study area. The Seyhan Basin is an important geographical region in southern Turkiye, located southwest of the \u0026Ccedil;ukurova delta complex in the Eastern Mediterranean region. The basin extends along the Seyhan River from Sivas to the eastern Mediterranean Sea, with a basin area of 22,035 km2, representing 2.07% of Turkiye's land area. The Seyhan River is one of the largest of Turkiye's rivers flowing into the Mediterranean Sea with a length of 560 km and a catchment area of 20450 km2. The coastline of the Seyhan Basin has changed over time due to geographical features, climatic influences, and human activities. These changes have had impacts on ecosystems in the coastal zone and shaped the use of water resources.\u003c/p\u003e \u003cp\u003eThe geographical location of the basin is in the form of a wedge extending northward from \u0026Ccedil;ukurova. Seyhan Basin is located between 36\u003csup\u003eo\u003c/sup\u003e 30' and 39\u003csup\u003eo\u003c/sup\u003e 15' north latitude and 34\u003csup\u003e0\u003c/sup\u003e 45' and 37\u003csup\u003e0\u003c/sup\u003e 00' east longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This basin, whose soil structure is generally mountainous, starts from the low and flat \u0026Ccedil;ukurova base in the south and continues with high mountainous areas and hilly sections towards the north. The climate in the coastal parts of the basin is hot and dry in summers and mild and rainy in winters. The vegetation of the basin varies depending on climate, geology, soil, and landforms. In the arid northern parts, grasses and meadows, and occasionally oak and oak shrubs are found, while shrub and maquis communities specific to the Mediterranean climate are encountered as you go south.\u003c/p\u003e \u003cp\u003eSeyhan Basin is one of the important regions of Turkiye with its rich water resources, diverse climatic characteristics, and agricultural potential. In the study, the coastal region was divided into three main zones to interpret the results of the analysis in a more qualified regional sense. The first zone is the lower coastal zone of Akyatan Lagoon, a Ramsar site. The second zone defines the lower coastal zone of Tuzla Lake. The third zone is defined as the Seyhan River, the mouth of the delta where the river meets the sea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Coastal boundary determination\u003c/h2\u003e \u003cp\u003eGEE, which was used in this study for automatic extraction of the Seyhan basin coastline in vector format, is a JavaScript-based platform that provides a user-friendly tool for web-based analysis. This facilitates fast processing and analysis of environmental data. The popularity and easy learnability of JavaScript allow users to quickly adapt to the platform (Pano et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, JavaScript's dynamic type system supports rapid prototyping and the development of code. The flexibility of the platform makes it compatible with various web-based technologies and libraries, which allows it to easily integrate external data sources and analysis tools. In this study, the GEE platform was effectively utilized for the definition of input parameters and the automatic extraction of coastal boundary lines. In addition, AWEI, an index developed in 2014 (Feyisa et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), was used in this study to automatically distinguish between water and land from satellite imagery. This index is derived from satellite imagery, especially Landsat, and is widely used to detect water surfaces (Guo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fisher et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Isiacik Colak et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wicaksono and Wicaksono \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AWEI highlights differences between water and land using a combination of pixel values in various spectral bands. It works specifically on spectral bands such as blue, green, red, and near-infrared and calculates the relationship between pixel values in these bands. The mathematical formula is as in equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${AWEI}_{TM}= 4*({ {\\rho }}_{Band2}-{ {\\rho }}_{Band5})-(0.25*{ {\\rho }}_{Band4}+2.75*{ {\\rho }}_{Band7})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eρ\u003c/em\u003e is the reflectance value of the spectral bands of Landsat 5 TM: \u003cem\u003eband\u003c/em\u003e 2 (green), \u003cem\u003eband\u003c/em\u003e 4 (NIR), \u003cem\u003eband\u003c/em\u003e 5 (SWIR), and band 7 (SWIR).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${AWEI}_{OLI}= 4*({ {\\rho }}_{Band3}-{ {\\rho }}_{Band6})-(0.25*{ {\\rho }}_{Band5}+2.75*{ {\\rho }}_{Band7})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eρ\u003c/em\u003e is the reflectance value of the spectral bands of Landsat 8 OLI: \u003cem\u003eband\u003c/em\u003e 3 (green), \u003cem\u003eband\u003c/em\u003e 5 (NIR), \u003cem\u003eband\u003c/em\u003e 6 (SWIR), and band 7 (SWIR).\u003c/p\u003e \u003cp\u003eEach pixel value contributes to the calculation of the index by multiplying it by a specific weight. AWEI is used as an important tool in coastal boundary extraction. By emphasizing the distinct differences between water and land, this index provides an effective method to accurately identify water surfaces (Bishop-Taylor et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Figliomeni et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Kalman filtering and statistical analysis in DSAS\u003c/h2\u003e \u003cp\u003eDSAS (2022) is an add-on software tool to ArcGIS used in coastal areas and is designed to analyze coastal changes. DSAS makes it possible to perform spatial analysis with different statistical rate techniques using digital data from the shoreline. End Point Rate (EPR) calculates the rate of shoreline change between two-time points. It is a very common method used in the literature in coastal boundary change studies. This method describes coastal change using the direct distance between the start and endpoints. Net Shoreline Movement (NSM) measures the net change of the shoreline over a given time interval. It assesses coastal change by calculating the difference between the start and endpoints. The Shoreline Change Envelope (SCE) assesses the curvature of the shoreline. It identifies changes in coastal morphology by measuring the curvature along the shoreline. Linear Regression Rate (LRR) is a least squares method that determines how linearly the shoreline changes over time. This method determines the trend of coastal change using time-series data. It uses distance and time as variables. Weighted Linear Regression (WLR) weighted the temporal change of the coastline. This method analyzes coastal change by assigning different weights to the rates of change over different periods. In weighted linear regression analysis, the importance of data points is related to measurement uncertainty. More reliable data are given more weight, i.e., points with lower measurement uncertainty are more important. The weight (w) is defined as the inverse square of the measurement uncertainty, i.e., the smaller e, the greater the weight. In the study, all coastlines were considered in the analysis process, and distance and time were used as variables.\u003c/p\u003e \u003cp\u003eAnalyzing the change of the coastline over time and predicting the future state is critical for coastal management and planning. Coastal management experts and planners need reliable and effective tools to predict the future position of the coastline. DSAS v.5 uses a Kalman filter-based approach to shoreline forecasting. This approach offers the ability to predict shoreline positions for the next 10 and 20 years based on historical shoreline position data (Kalman \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). The DSAS Kalman Filter Technique is initialized using a calculated linear regression ratio. This ratio is determined using a linear regression analysis of historical shoreline data. It then calculates the position and rate of change of the shoreline at a step interval of one-tenth of each year (Long and Plant \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The forecasting process involves the Kalman Filter analyzing the difference between modeled and observed shoreline positions, adding speed and uncertainty to the predictions. However, this approach has limited capacity to deal with uncertainties and may not adequately reflect some complex coastal dynamics. Also, if there are fewer than four coastline data, accurate forecasts may be difficult (Ciritci and Turk 2020). In this study, coastlines from nine different years were used as input data. The shoreline forecasts for the years 2033 and 2043 were made with the Beta Forecast tool using the Kalman Filter Model in DSAS. The forecasted coastline data were mapped together with uncertainty polygons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Kruskal-Wallis H Test and analysis of Variance (ANOVA)\u003c/h2\u003e \u003cp\u003eThe main criterion for determining the correct method for statistical data analysis is the type of data. Sample size, normal distribution of the data, and homogeneity of variance play an important role in determining whether the data is parametric or nonparametric. If the data set has a sample size of more than 30, a normal distribution, and a homogeneity of variance, it is parametric. For parametric data, a t-test (two groups) or ANOVA (more than two groups) is used. For nonparametric data, chi-square tests are used to compare between two groups, and the Kruskal-Wallis H test is preferred if the number of groups is more than two.\u003c/p\u003e \u003cp\u003eIn this study, to determine the temporal and geometric changes in the coastline of the Seyhan basin, which is significantly affected by climate change and drought in Turkiye, spatial analyses were carried out over the years using statistical techniques such as EPR, LRR, NSM, SCE, and WLR with the DSAS tool. With these techniques, it was tried to determine whether the results obtained from the changes in the coastline using different perspectives are statistically significant, that is, whether they contain similar results. If the Kruskal-Wallis H test or ANOVA results show that there is a difference between the groups, multiple comparisons should be made to determine which groups this difference originates from. If sample size, normal distribution, and homogeneity of variance are provided, Tukey's test can be used; if not, Tamhane's T2 can be used to determine which groups are different. SPSS software was used in these analyses. SPSS obtains the p-value at the 95% significance level from the F table. If the p-value obtained is less than 0.05, it can be concluded that there are statistically significant differences between the variables (Kalaycı \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the framework of this methodology, we adopted an approach focused on selecting appropriate analysis methods to assess the statistical significance of the results obtained. The data set for each technique was examined in detail to determine whether it had a normal distribution and homogeneous variance. This assessment was carried out to ensure the reliability and generalizability of the statistical results. To understand the effects of statistical measurement methods on the data set and to increase the reliability of the results, normal distribution and variance homogeneity analyses were performed. In this study, the Kruskal-Wallis H test, ANOVA, and multiple comparison analyses were performed to determine whether the temporal and geometric changes in the coastline in the Seyhan basin obtained from different techniques are statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Min-Max normalization\u003c/h2\u003e \u003cp\u003eMin-max normalization contributes to the evaluation of data in different units. This method expresses each feature in the data set on the same scale. Features measured in different units or varying in different ranges become comparable because they are expressed on the same scale, thanks to this normalization technique. Features measured in different units are on the same scale when Min-Max normalization is applied. This ensures that each feature contributes equally during the training of the model. For example, if one feature is measured in meters and the other feature is measured in miles, thanks to Min-Max normalization, they will both be in the range (0, 1) and will contribute similarly to the training process. Having values in different units on the same scale allows for more accurate comparisons between them. However, it is important to consider whether the normalization is appropriate depending on the data set and the algorithms used. The formula for Min-Max normalization is as in Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Jain et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${X}_{normalized}=\\frac{X-{X}_{min}}{{X}_{max}-{X}_{min}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eX\u0026thinsp;=\u0026thinsp;original feature value,\u003c/p\u003e \u003cp\u003eX_min\u0026thinsp;=\u0026thinsp;the minimum value of the property,\u003c/p\u003e \u003cp\u003eX_max\u0026thinsp;=\u0026thinsp;the maximum value of the property,\u003c/p\u003e \u003cp\u003eX_normalized\u0026thinsp;=\u0026thinsp;normalized feature value.\u003c/p\u003e \u003cp\u003eIn this study, NSM and SCE are the distance measurement models, and EPR, LRR, and WLR are the statistical measurement methods. Deposition and erosion values obtained by statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR are in meters or meters/year. Min-max normalization was applied to statistically compare the results obtained from these techniques.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eTo determine the temporal and geometric changes in the coastline of the Seyhan basin in Turkiye, which is significantly affected by climate change and drought in Turkiye, comparative statistical and performance analyses were applied to the results obtained with statistical proportioning techniques such as different EPR, LRR, NSM, SCE, and WLR with the DSAS tool. The coastline of this basin is divided into three main regions. In addition, by using the Kalman filtering model, the shorelines of the years 2033 and 2043 were predicted, and the vulnerable areas in terms of erosion and deposition on the future shoreline were determined. Again, the Kruskal-Wallis H test and ANOVA tests were used to determine whether there is a statistically significant difference between the results obtained using different methods of coastal change. All the findings obtained in the study are explained in detail under the sub-headings in this section.\u003c/p\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1. Coastal change analysis results\u003c/h2\u003e\n \u003cp\u003eIn this paper, as the first step in defining the input parameters, the GEE platform was used to automatically extract the coastline of the 560 km-long Seyhan Basin in vector format using satellite images from 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2023 every five years (Fig. \u003cspan\u003e3\u003c/span\u003e). Landsat satellite data collections were used for satellite images. The boundary geometry of the study area was determined from the image collection, and the images were filtered by defining other necessary parameters (cloudiness, snow cover ratio, and date). Image composites were created for each year by using the annual median composites of the available satellite images in a one-year time interval as the date. Cloud masking was performed on the filtered images to enhance the images. In the second step, AWEI, an index developed to automatically distinguish between water and land from satellite images, was used in the coastal extraction process. The Otsu thresholding method, which can automatically threshold the generated images, was used to separate the image into two different classes (binary images). Coastal boundaries were automatically extracted in vector format (.shp) from the obtained images on the platform.\u003c/p\u003e\n \u003cp\u003eThe entire data set was exported and transferred to the ArcGIS 10.6 environment, and the third step, the DSAS modeling phase, was started. First, a personal database was created, and the entire data set was stored here. Then a baseline was created for the analysis with buffer analysis. This baseline provides a basic reference point for the calculation of coastal changes. Using the baseline, 970 transect lines were created on the shorelines at 50-meter intervals. All these input data were processed into the model, and EPR, LRR, NSM, and WLR analysis results were calculated and a table was created. Negative values in the analysis results define the presence of erosion, and positive values define coastal accretion. To define this change between 1985 and 2023 more clearly, the study area was represented by three zones according to block boundaries. All statistical results are presented in Figs.\u0026nbsp;4\u0026ndash;\u003cspan\u003e6\u003c/span\u003e for each zone.\u003c/p\u003e\n \u003cp\u003eTo better interpret the EPR, LRR, WLR, and NSM analysis result data and graphic values presented above, they were evaluated in 7 basic classes. In the classification, red indicates extremely high erosion, orange indicates severe erosion, light orange indicates moderate erosion, yellow indicates no change areas, light green indicates moderate coastal deposition, green indicates severe deposition, and dark green indicates extremely severe deposition. SCE analysis result data and graphic values were evaluated in four basic classes. No change zones are shown in dark green, moderate change in green, severe change in yellow, and very severe change zones in red. The table below (Table \u003cspan\u003e2\u003c/span\u003e) shows the results of EPR, LRR, WLR, SCE, and NSM model analysis for the shoreline change rates in the Seyhan basin coast from 1985 to 2023.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eShoreline change rates obtained from EPR, LRR, NSM, WLR, and SCE analyses (1985\u0026ndash;2023)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTransect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSCE* (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNSM* (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEPR* (m/yr)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLRR* (m/yr)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWLR* (m/yr)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1022.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e997.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1430.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e496.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1382.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e456.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u0026ndash;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u0026ndash;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u0026ndash;250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251\u0026ndash;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301\u0026ndash;350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351\u0026ndash;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401\u0026ndash;450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451\u0026ndash;492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493\u0026ndash;550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-91.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e551\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601\u0026ndash;650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651\u0026ndash;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e701\u0026ndash;750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751\u0026ndash;765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e766\u0026ndash;800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e801\u0026ndash;850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-263.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e851\u0026ndash;900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1311.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e583.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1129.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-394.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900\u0026ndash;950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1449.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e905.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1301.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1154.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-144.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951\u0026ndash;970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1453.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1337.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"18\"\u003e\n \u003cp\u003e\u003cstrong\u003e*SCE and NSM methods are in meters, other methods are in meters/year.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the coastal change analysis for Zone 1, 492 transect lines were created with 50-meter intervals (Trans No: 1-492), and all analyses were evaluated over this line. When the results of the DSAS analysis between 1985 and 2023 are examined, it is determined that there is mostly coastal accretion along the transect line 0-100 and intense coastal erosion along the remaining transect line. This situation constitutes an important factor that threatens Akyatan Lagoon, which is evaluated within the scope of the Ramsar Convention, is the lagoon with the largest area in the \u0026Ccedil;ukurova delta and is one of the important migration points for birds. Akyatan Lagoon is the largest lagoon in Turkiye with an area of 7420 ha. Findings for the NSM method: The maximum coastal advance determined on the 51\u0026ndash;100 transect line was 1382.39 m, and the minimum coastal erosion determined on the 351\u0026ndash;400 transect line was 76.43 m. Similarly, in the results of the SCE method, the maximum coastal advance determined on the 51\u0026ndash;100 transect line was determined to be 1430.63 m for this region. Among the other methods evaluated proportionally, maximum erosion was determined as 2.01 m/yr (transect line 351\u0026ndash;400) and maximum accretion as 36.38 m/yr (transect line 51\u0026ndash;100) for the EPR method. Similar results were calculated using the LRR method. The maximum erosion and accumulation rates were \u0026minus;\u0026thinsp;1.80 m/yr (transect line 351\u0026ndash;400) and 9.00 m/yr (transect line 51\u0026ndash;100), respectively. For the WLR method using the weighted ratio method, the maximum erosion rate was \u0026minus;\u0026thinsp;2.16 m/yr (transect line 301\u0026ndash;350), and the maximum accumulation rate was 14.31 m/yr (transect line 51\u0026ndash;100). In general, moderate erosion, stable, and high deposition rates were observed in Zone 1. The reason for the high accretion rate is that the water channel in the region has dried up over time and the shoreline has moved out. A visible drought was observed in this accumulation zone.\u003c/p\u003e\n \u003cp\u003eA total of 273 transect lines were established covering transect numbers from 493 to 765 in Zone 2. When the findings are evaluated in general for all methods, low erosion, stable, and low deposition zones are observed along the coast. When the results of DSAS analysis are analyzed, according to EPR and LRR methods, maximum erosion occurred at transect lines 493\u0026ndash;550 and was determined as -2.39 m/yr and \u0026minus;\u0026thinsp;2.45 m/yr, respectively. The maximum accumulation occurred on transect lines 751\u0026ndash;765 with 0.99 m/yr and 0.89 m/yr. The amount of deposition and erosion in the region is quite low. WLR method results also support this situation. The maximum erosion rate was \u0026minus;\u0026thinsp;3.41 m/yr and the maximum coastal accretion rate was 0.71 m/yr. According to the findings of the NSM method, the maximum amount of erosion experienced in the region is 90.99 m (transect line 493\u0026ndash;550) and the maximum accumulation is 37.81 m (transect line 751\u0026ndash;765). According to the results of the SCE method, the maximum erosion is 94.82 m, which supports the NSM result. Tuzla Lake or Lagoon is separated from the Mediterranean Sea by a tombolo formed by a narrow and low dune accumulated on the shore, and the water inlet and outlet to the lagoon is provided by a narrow channel (Dural and G\u0026ouml;ksu, 2004:361). In this respect, it has been determined that there is a coastal change for zone 2 that will not affect the coastal zone and the lagoon near it much.\u003c/p\u003e\n \u003cp\u003eIn the coastal change analysis for the Seyhan delta mouth, which is defined as Zone 3, 205 transect lines were created (Trans No: 766\u0026ndash;970) and all analyses performed for this region were evaluated over this line. According to the DSAS analysis results, it was determined that the most change was experienced in this region. When the findings are analyzed, the greatest change occurred in the transect line between 900\u0026ndash;970, which coincides with the mouth of the delta. According to the results of the NSM method, the maximum accumulation was 1337.72 m and the maximum erosion amount was 1301.4 m. SCE method results also show that the maximum erosion was 1453.65 m. In the Seyhan Delta mouth, the maximum change determined according to the DSAS analysis results is due to the erosion process under the influence of various geological, hydrological, and anthropogenic factors. Water flow, natural erosion, tidal effects, and human impact play a decisive role in the erosion and erosion of the delta mouths in the region. According to the LRR method, the maximum erosion rate was calculated as -39.0 m/yr and the maximum deposition rate as 28.06 m/yr. The severity of erosion and deposition is quite severe here. The results of the EPR method also support the results of the other methods, with a maximum erosion rate of -36.8 m/yr and a maximum deposition rate of 35.20 m/yr. Finally, according to the findings obtained from the WLR method, which is a weighted ratio method, the maximum erosion rate is -40.9 m/yr and the maximum deposition rate is 29.40 m/yr.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 Min-Max normalization results\u003c/h2\u003e\n \u003cp\u003eThe deposition and erosion values obtained by the different statistical proportioning techniques used in this study, such as EPR, LRR, NSM, SCE, and WLR, are in meters or meters/year. Min-max normalization was applied to statistically compare the results obtained from these techniques. In this way, features measured in different units or varying in different ranges are made comparable because they will be expressed on the same scale, thanks to this normalization technique. Thanks to Min-Max normalization, the values obtained from each technique will be in the range (0, 1). In this way, the values in different units are on the same scale, allowing more accurate comparisons to be made between these values. In this study, Min-Max normalization was applied to the deposition and erosion values obtained from these techniques for three regions created in the Seyhan basin before method-based comparative statistical analysis was performed. Descriptive statistics of min-max normalization results for these 3 regions are given in Table \u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics of min-max normalization results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAREA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% Confi. Int. for Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Accuracy performance analysis of the Kalman Filtering method\u003c/h2\u003e\n \u003cp\u003eIn order to evaluate the statistical accuracy of the Kalman filter method used in the future prediction of the coastline, a performance analysis was also performed in the study. In the performance analysis, min-max normalization was performed first to make a method-based comparison. In this context, statistical values were obtained by comparing the automatically extracted coastlines of the study area for the years 1985-1990-1995-1995-2000-2000-2005-2010-2015-2020 and 2023 with the estimated coastlines obtained by the Kalman filtering technique on the same date. In the method, coastal data including at least four different time periods should be used in coastline estimation. In the study, 10- and 20-year forecasts were performed in two different scenarios.\u003c/p\u003e\n \u003cp\u003eIn the first stage of the accuracy assessment process, 10-year coastline forecasts for four different years were made for the study area. These are respectively: 2005 coastline prediction using 1985\u0026ndash;1995 coastline data; 2010 coastline prediction from 1985\u0026ndash;2000; 2015 coastline prediction from 1985\u0026ndash;2005; and 2020 coastline prediction from 1985\u0026ndash;2010. The 2005, 2010, 2015, and 2020 predicted shoreline analysis results were compared with the existing automatically extracted shorelines. The 10-year predicted coastlines for the study area were processed into the model, and the descriptive statistical values for each region defined in the Seyhan basin coastline according to the EPR, LRR, NSM, and WLR analysis results are given in Table \u003cspan\u003e4\u003c/span\u003e below. Figure \u003cspan\u003e7\u003c/span\u003e shows the graphs of RMSE values obtained as a result of the comparisons made for these methods according to different years.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistical values of 10-year projected shorelines\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen the above results are evaluated in general, SCE, NSM, and EPR methods show higher performance in Zone 1 compared to other methods. Especially SCE and NSM seem to be close to each other until 2020. The LRR method continues to decrease its performance until 2015, but shows an increase in performance in the following years. The WLR method generally performs better in Zone 1 than in other zones. In this zone, it shows a decline from 2015 to 2020. In Zone 2, the SCE, NSM, and EPR methods show generally high performance from 2005 to 2020. The performance of these methods remains fairly stable and at a high level. While the LRR method performs poorly until 2010, its performance improves in the following years. The WLR method, which has the lowest performance among these methods, continues to increase its performance until 2020. In Zone 3, the performance of all methods decreases compared to Zone 2 and Zone 1. The SCE, NSM, and EPR methods show low performance from 2005 to 2020. However, they have higher values compared to Zone 2. The LRR method continues to improve its performance until 2020. The WLR method generally underperforms, but its performance improves in 2020. In conclusion, according to the given data, there are different trends in the performances obtained with different measurement methods in Zone 1, Zone 2, and Zone 3 over the years. More detailed analyses can be made of the causes and consequences of these trends. These interpretations may vary depending on the characteristics, data sets, and use cases of each region. It can play an important role in the correct selection and analysis of measurement methods and regions.\u003c/p\u003e\n \u003cp\u003eIn addition, 20-year coastline forecasts were made for two different years in the study area. These are, respectively, 2015 coastline estimation using 1985\u0026ndash;1995 coastline data and 2020 coastline estimation using 1985\u0026ndash;2000 coastline data. The 2015 and 2020 predicted shoreline analysis results were compared with the existing automatically extracted shorelines. The 20-year predicted shorelines for the study area were processed into the model, and the descriptive statistical values for each region defined in the Seyhan basin coastline according to the EPR, LRR, NSM, and WLR analysis results are given in Table \u003cspan\u003e5\u003c/span\u003e below. Figure \u003cspan\u003e8\u003c/span\u003e shows the graphs of the RMSE values obtained as a result of the comparisons made for these methods according to different years.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistical values of 20-year projected shorelines\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the tables and graphs above, in Zone 2 for 2015, the highest values are observed in the coastal boundary change performance with the SCE method, while significant values are obtained in the determinations made with the NSM and EPR methods. Other methods have higher performance. In Zone 3, the coastal boundary change performance with the SCE method decreased the most. It varies with other methods. In 2020, in Zone 2, the performances with the SCE method showed the highest performance compared to other methods. In other methods, the results of the NSM and EPR methods are closer to each other. In Zone 3 and Zone 1, there is a general downward trend in performance. In general, in Zone 2 and Zone 3, the results in 2020 are better than in 2015, while in Zone 1, SCE and EPR methods show a reverse performance decrease. Especially in Zone 3, there is a general downward trend in performances, but this trend varies across methods.\u003c/p\u003e\n \u003cp\u003eIn addition, the Kalman filter model was analyzed to determine the performance of the prediction time in 10-year and 20-year future forecasts for coastline delineation for the years 2033 and 2043. As an example, the method-based performances of 10-year and 20-year future forecasts for 2015 are given in Fig. \u003cspan\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIn the figure above, when comparing the 10-year and 20-year performances of different methods in different zones, important findings have been reached. In Zone 1, the results with SCE, NSM, and EPR methods show that 10-year performances are generally better than 20-year performances. However, a different trend was observed with the LRR and WLR methods, with 10-year performances generally better than 20-year performances. In Zone 2, no significant change was observed between 10 and 20-year performances for the measurements with the SCE method. Results with NSM and EPR methods show that 10-year performances are generally better than 20-year performances. However, different trends are observed for the LRR and WLR methods. In Zone 3, for all measurement methods, 10-year performances are generally better than 20-year performances. This suggests that the long-term performance of the methods in Zone 3 is worse. These results provide important clues for understanding the long-term performance of a given method and for optimizing measurement processes. In general, when the RMSE values obtained from 10-year and 20-year forecasts are analyzed, it is seen that less time in the future forecast affects the performance in determining coastal boundary changes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.4 Coastal future forecast for 2033 and 2043\u003c/h2\u003e\n \u003cp\u003eBased on the results of the obtained shorelines and rates of change, the Kalman filter model was used to predict the future shoreline for the years 2033\u0026ndash;2043. In this way, it is aimed at providing a scientific basis for reducing environmental risks in the region, protecting natural resources, and promoting sustainable development in coastal areas. The future coastline forecast model was simulated using the beta forecast tool in the DSAS tool. In Fig. \u003cspan\u003e10\u003c/span\u003e, in addition to the 1985\u0026ndash;2023 shoreline data, the estimated shoreline data for the years 2033 and 2043 are presented in vector form.\u003c/p\u003e\n \u003cp\u003eIt also takes into account possible uncertainties in the simulated shorelines. In estimation methods such as the Kalman filter, uncertainty may be due to the model used or measurement uncertainties. The model used may not have complete information about the real system or may not fully represent all effects in the system. In this case, uncertainties in the model can affect the accuracy of future predictions. During the actual measurement process, there may be uncertainties in data such as shorelines and rates of change. Measurement errors can be related to the accuracy or precision of the measuring instruments. These uncertainties should be accounted for in estimation methods such as the Kalman filter and taken into account to assess the reliability of the estimates. Uncertainty can be expressed in terms of the confidence intervals of the predictions or a given confidence level. In this way, more information about how reliable the forecasts are can be provided and help decision-makers make the right decisions. In the study, model analysis was performed for all methods to determine possible coastal changes for the rate of change and \u0026plusmn;\u0026thinsp;uncertainty values at a 95% confidence interval. Future coastline predictions for the years 2033\u0026ndash;2043 using the Kalman filter model are given in Table \u003cspan\u003e6\u003c/span\u003e below.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe results of the coastal change analysis performed with the estimated coastlines are explained separately for the years 2033 and 2043. According to the DSAS report generated as a result of the analysis, the coastal boundary changes estimated for 2033 show various trends in the coastal dynamics in the Seyhan Delta region. The SCE shows an average change of 189.05 meters over a total of 485 transects. The largest change was recorded in Zone 3 (transect 479) with 1488.62 meters, and the smallest change was recorded in Zone 1 (transect 1) with 1.57 meters. The NSM shows a decline in 73.4% of the total number of transects (with an average change of -16.41 meters) and a growth of 26.6% (with an average change of 225.16 meters). The largest regression was observed in Zone 3 (transect 465\u0026apos;) with \u0026minus;\u0026thinsp;1476.17 meters. The EPR shows an average regression of -0.69 meters per year. While a statistically significant regression was detected in 73.4% of the transects experiencing erosion, the average erosion value of these transects was calculated as -2.69 meters/year. In 26.6% of the transects that experienced accumulation, an average growth of 4.81 meters/year was observed. LRR shows an average regression of -1.27 meters/year. A statistically significant regression was detected in 75.26% of the transects experiencing erosion, and the average erosion value of these transects was calculated as -2.68 meters/year. In 24.74% of the transects that experienced accumulation, an average growth of 3.03 meters/year was observed. WLR shows an average regression of -1.98 meters/year. A statistically significant regression was detected in 79.38% of the transects experiencing erosion, and the average erosion value of these transects was calculated as -3.05 meters/year. In 20.62% of the transects experiencing accretion, an average growth of 2.12 meters/year was observed. These data provide a general summary of the expected shoreline changes in 2033. In a period dominated by erosion, some areas experienced significant growth. However, there is a certain margin of uncertainty for each type of rate, and these results provide a complex picture of the coastal dynamics in the Seyhan Delta region, with shoreline changes estimated for the year 2033.\u003c/p\u003e\n \u003cp\u003eFor the year 2043, the changes in the analyzed shorelines generally reflect the erosion trend. However, local accumulation is observed in some specific areas. For example, according to SCE data, the average change along the delta is 155.07 meters, but the large difference between the minimum and maximum values is striking. In particular, the maximum distance of up to 1484.03 meters indicates a marked retreat in some parts of the coast. The NSM data show a negative movement in general (81.86%) and a positive movement in particular at 88 transects (18.14%). This positive movement may indicate that sediment deposition in certain areas of the Seyhan Delta has caused localized widening of the shorelines. On the other hand, when we focus on erosion rates, we see that the average erosion rates calculated by the EPR, LRR, and WLR methods are \u0026minus;\u0026thinsp;2.88, -3.05, and \u0026minus;\u0026thinsp;2.92 meters/year. These figures indicate a rapid retreat along the coast in general. It is particularly noteworthy that the maximum erosion rate of transect 441 is -49.47 meters/year, highlighting the serious erosion problem in some areas along the coast. As a result, changes in the Seyhan Delta coastal margin indicate erosion as a general trend, while local accumulation is observed in certain places. Especially for Akyatan and Tuzla lagoons, which are considered within the scope of the Ramsar site, located next to Zone 1 and Zone 2, and in which biodiversity is high, it is predicted that there will be more erosion-prone areas that will lead to significant losses.\u003c/p\u003e\n \u003cp\u003eWhen the rates of coastal change according to the forecasts made between 2033 and 2043 are compared, it is seen that the rate of coastal change decreases in 2043, but coastal erosion becomes more pronounced. Evaluation of factors such as average coastal change, net coastal movement, endpoint velocity, linear regression velocity, and weighted linear regression velocity indicates that coastal erosion will increase in 2043, which can be attributed to climate change and environmental impacts. These findings provide important insight into the management and protection of delta coasts and can help develop strategies to cope with the impacts of coastal erosion.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.5 ANOVA Test Results with Kruskal-Wallis H Test\u003c/h2\u003e\n \u003cp\u003eNormal distribution and homogeneity of variance tests were performed to determine whether the data sets of the methods to be compared for coastal boundary change are parametric. In this study, the choice of parametric or non-parametric methods was made by considering the potentially misleading results of normal distribution and homogeneity. For this purpose, the non-parametric Kruskal-Wallis H test and the parametric ANOVA test were applied. These analyses were applied to obtain reliable and comprehensive results, depending on the distributional characteristics of the data set. By comparing the results of both analysis methods, it was determined whether there is a statistically significant difference between the temporal and geometric changes in the coastline in the Seyhan basin. In addition, subgroups were formed between different proportioning techniques according to the accumulation and erosion rates defined as dependent variables with SPSS software. These subgroups were created to determine whether the method-based results show the same or different characteristics in the coastal boundary changes over the years within the study area. Table \u003cspan\u003e7\u003c/span\u003e shows the results of the homogeneity of variance test and the normal distribution test.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Future coastline predictions for the years 2033-2043\u003c/div\u003e\n \u003cdiv align=\"left\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1717492519.png\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eHomogeneity of variance and normal distribution test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eTests of Normality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAREA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eKolmogorov-Smirnova\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eShapiro-Wilk\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e. \u003cem\u003eLilliefors Significance Correction\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of Homogeneity of Variances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAREA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevene Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003edf1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003edf2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen the results obtained from the homogeneity of variance and normal distribution tests for the accumulation and erosion rates in the 3 main regions in the Seyhan basin are analyzed, it is determined that the coastal change rates in all regions are not normally distributed. Since the values in the Sig. column of this table are less than 0.05, it is determined that the coastal change rates in all regions are not normally distributed. Except for Zone 2, the variances in accretion and erosion rates in the other zones are not homogeneous. In this study, statistical comparisons should be made according to the Kruskal-Wallis H test according to normal distribution and variance homogeneity. However, considering the potentially misleading results of normal distribution and homogeneity in data analysis, both the Kruskal-Wallis H Test and ANOVA analysis were performed in this study, and the results are given in Tables \u003cspan\u003e8\u003c/span\u003e and \u003cspan\u003e9\u003c/span\u003e below.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKruskal-Wallis H Test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistics\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZONE 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZONE 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZONE 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e785.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymp. Sig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ea. Kruskal Wallis Test b. Grouping Variable: method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of variance (ANOVA) test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAREA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eZONE 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen the Kruskal-Wallis H Test or ANOVA results and the averages of the groups are evaluated together, it is seen that the value in the Sig. column in the tables is less than 0.05. These results indicate that the results obtained from the techniques used to determine the temporal and geometric changes in the coastline in the Seyhan basin are different; that is, there is a statistically significant difference. However, the results of the Kruskal-Wallis H test or ANOVA test do not contain information about which groups these differences are between. For this reason, multiple comparisons (post-hoc tests) were made to determine which techniques the difference was between. In the following tables (Tables \u003cspan\u003e10\u003c/span\u003e\u0026ndash;\u003cspan\u003e12\u003c/span\u003e), pairwise comparisons of the techniques with or without a statistically significant difference between them are given according to Tukey HSD and Tamhane\u0026apos;s T2 test.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 10\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise comparisons by method for Zone 1 (Tukey HSD and Tamhane\u0026apos;s T2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple Comparisons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZONE 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTukey HSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTamhane\u0026rsquo;s T2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(I) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(J) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eThe mean difference is significant at the 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 11\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise comparisons by method for Zone 2 (Tukey HSD and Tamhane\u0026apos;s T2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple Comparisons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZONE 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTukey HSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTamhane\u0026rsquo;s T2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(I) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(J) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eThe mean difference is significant at the 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 12\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise comparisons by method for Zone 3 (Tukey HSD and Tamhane\u0026apos;s T2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple Comparisons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZONE 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTukey HSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTamhane\u0026rsquo;s T2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(I) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(J) method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference (I-J)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eThe mean difference is significant at the 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs an example, when Table \u003cspan\u003e11\u003c/span\u003e, which shows the multiple comparisons in Zone 2, is examined, it is statistically determined that the SCE technique shows similar characteristics with NSM and EFR and different characteristics with LRR and WLR in the multiple comparisons made between statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR with the DSAS tool to determine the temporal and geometric changes in the coastline in the Seyhan basin. The NSM technique shows similar characteristics with SCE and EPR but different characteristics with LRR and WLR. The EPR technique shows similar characteristics with SCE and NSM and different characteristics with LRR and WLR. The LRR method shows different characteristics from other methods, except WLR. Finally, it is statistically determined that the WLR method is similar to LRR and different from other methods. In addition, according to the different techniques by which the accumulation and erosion rates, which are defined as dependent variables, were calculated, the subgroups specified in Table \u003cspan\u003e13\u003c/span\u003e were formed for the three regions forming the coastline in the Seyhan basin.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 13\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSubgroups between different techniques used in determining coastal changes according to three different regions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eTukey HSDa ( Subset for alpha\u0026thinsp;=\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZONE 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZONE 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eZONE 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeans for groups in homogeneous subsets are displayed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003ea. Uses Harmonic Mean Sample Size = (ZONE 1\u0026thinsp;=\u0026thinsp;492, ZONE 2: 273, ZONE 3: 205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo determine whether statistical proportioning techniques such as EPR, LRR, NSM, SCE, and WLR have similar characteristics to determine the temporal and geometric changes in three different coastlines in the Seyhan basin, subgroups were formed. In Zone 1, two subclasses were formed. In Zone 2, SCE, NSM, and EPR have similar characteristics, while WLR and LRR techniques differ from other techniques. In Zone 3, three different subclasses were formed. SCE and LRR formed a subclass on their own. These methods produce results that differ from each other. NSM, EPR, and WLR show similar characteristics. While SCE is different from the other methods, the results of the other methods are similar to each other. In general, when the subgroups obtained from these 3 regions are evaluated, similar results are obtained in determining coastal changes with NSM and EPR techniques. The results of other techniques have different or similar characteristics, varying according to the region. The SCE method differs from other methods in Zone 1 and Zone 3. The LRR method is similar to the WLR method in Zone 2 and differentiates from other methods in Zone 3. In Zone 1, it is similar to the other methods except for the SCE method.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eCoastal areas are sensitive regions rich in natural resource potential and biodiversity, offering important economic opportunities for society but also under development pressure. Coastal areas, where settlements and civilizations have historically developed and where cultural and economic interactions have been intense, are today facing various use demands along with coastalization trends. Urban uses, industrial facilities, energy terminals, shipyards, touristic facilities, fishing, and other activities increase competition in coastal areas. However, meeting these demands in an unplanned manner leads to incompatible use patterns, the destruction of natural resources, and the deterioration of the ecological balance. These unplanned development trends cause social, economic, environmental, and spatial problems. Environmental problems such as degradation of ecosystems in coastal areas, loss of balance between marine and terrestrial ecosystems, erosion, and sea level rise arise. At the same time, conflicts between communities living in coastal areas are increasing in terms of site selection, resource utilization, and urban planning. In this context, it is of great importance to adopt a planned and multi-stakeholder approach in line with sustainable development goals in coastal areas. Developing an integrated planning and management strategy for the protection of coastal areas, sustainable use of natural resources, restoration of ecosystems, and economic well-being of communities will contribute to solving problems in coastal areas.\u003c/p\u003e \u003cp\u003eThis study is designed to examine the changes in the coastline of the Seyhan Delta between 1985 and 2023, to understand the causes and effects of these changes, to analyze them from a scientific perspective, to determine the positions of the coastline in the next 10 and 20 years, to compare statistical results by making predictive modeling with the Kalman filtering technique, and to investigate the accuracy of this model. The coastal change rates determined in the study were analyzed by different parameters (EPR, LRR, WLR, NSM, and SCE) at 95% confidence intervals. According to the SCE model results, the maximum annual accumulation rate is 1453.65 m2, the NSM model results show that the maximum annual accumulation rate is 1382.39 m2, and the maximum erosion rate is -1301.41 m2. EPR model results show that the maximum annual accumulation rate is 36.38 m/yr and the maximum erosion rate is -36.85 m/yr; WLR model results show that the maximum annual accumulation rate is 29.40 m/yr and the maximum erosion rate is -40.87 m/yr; and finally, LRR model results show that the maximum annual accumulation rate is 28.06 m/yr and the maximum erosion rate is -39.00 m/yr. Different statistical analysis methods (Kruskal-Wallis H test, ANOVA, multiple comparison analysis) were used to evaluate the statistical significance of the results. To assess the statistical significance of the results, a focused approach was adopted to select appropriate analysis methods. The data set for each technique was examined in detail at the 95% significance level to determine whether it was normally distributed and homogeneous in variance. In the analyses performed in three different regions, Zone 1, Zone 2, and Zone 3, it was observed that SCE, NSM, and EPR techniques showed similar characteristics. The WLR and LRR techniques gave different results. In Zone 3, SCE and LRR formed their subclasses, while in Zone 1, methods other than SCE gave similar results. In general, NSM and EPR techniques provided similar results in determining coastal changes, but other techniques differed by region. In the study, the Kalman filter model used in the future prediction of the coastline was used to determine the coastline locations for the years 2033 and 2043 under two different scenarios. In addition, performance analysis was also performed in the study to evaluate the statistical accuracy of the model. In the study, important findings were obtained while comparing the 10-year and 20-year performances of different methods in different regions. When the RMSE values of the 10-year and 20-year forecasts are analyzed, it is seen that the longer prediction period in future forecasts negatively affects the performance in determining coastal boundary changes. This suggests that shorter-term forecasts tend to yield more reliable results. It is thought that shorter-term forecasts can more accurately predict coastal boundary changes and can be a more effective tool in managing these changes.\u003c/p\u003e \u003cp\u003eWhen all the analyses performed in the study are examined, it is determined that the rate of coastal change along the coast is gradually increasing, and this acceleration trend indicates erosion. According to the results of the DSAS analysis, it was determined that the most change was experienced in the Seyhan delta river mouth, defined as Zone 3. Erosion of delta mouths is a result of various geological, hydrological, and anthropogenic factors. Geologic factors here can occur under the influence of different geologic processes. In the Seyhan Delta, processes such as water flow, natural erosion, and tidal effects have been effective. Apart from this, various water movements, flow rate and intensity in the river, and sea level rise accelerate the erosion process. In addition, human impact, which is referred to as an anthropogenic factor, is also an important factor in the erosion of delta mouths. It has been determined that various activities, such as coastal development projects, the presence of sewage discharges, and agricultural and industrial wastes, trigger erosion in the region and affect natural processes. When the results of the study and regional characteristics are analyzed, it is necessary to take protective measures for the intervention of natural factors as well as artificial factors. Effective management plans should be developed, and existing strategies should be strengthened to prevent the destruction of the ecosystem and the restoration of vegetation along the coastline. Continuous nourishment of the areas used as beaches in the region will be effective in preventing coastal erosion.\u003c/p\u003e \u003cp\u003eAll academic research shows that monitoring and understanding the changes in coastal zones is critical for sustainable coastal management. These changes can be associated with factors such as erosion, coastalization, habitat loss, and other environmental impacts. Advanced remote sensing technologies provide important tools to detect and analyze these changes. In the future, these technologies are expected to develop further and become more widespread. This will make it possible to monitor changes in coastal zones more precisely, manage natural resources more effectively, and respond more quickly to environmental problems. Furthermore, future coastal change trends can be predicted through the use of remote sensing technologies. These predictions can shape the future use and protection of coastal areas, playing an important role in planning processes. Therefore, it is necessary to promote the use of remote sensing technologies in coastal areas and to evaluate these data in an integrated manner in line with sustainable development goals. This approach will undoubtedly play an important role in the future of coastal regions for the preservation of economic prosperity, sustainable use of natural resources, and conservation of biodiversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author is thankful to the U.S. Geological Survey for providing facilities to carry out this study by making the data available in open access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM\u0026uuml;nevver Gizem G\u0026uuml;m\u0026uuml;ş, conceptualized and designed the study, collected and analyzed the data, interpreted the results, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf accepted, this article will be funded as open access by the Scientific and Technological Research Council of Turkey (T\u0026Uuml;BİTAK).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbd-Elhamid HF, Zeleň\u0026aacute;kov\u0026aacute; M, Barańczuk J, Gergelova MB, Mahdy M (2023) Historical trend analysis and forecasting of shoreline change at the Nile Delta using RS data and GIS with the DSAS tool. 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Asil Yayın Dağıtım, Ankara, Turkiye, p 359\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-science-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esin","sideBox":"Learn more about [Earth Science Informatics](http://link.springer.com/journal/12145)","snPcode":"12145","submissionUrl":"https://submission.nature.com/new-submission/12145/3","title":"Earth Science Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"DSAS, Earth Engine, GIS, Kalman Filter, Kruskal-Wallis H Test, Shoreline Changes","lastPublishedDoi":"10.21203/rs.3.rs-4411235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4411235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComplex changes in coastlines are increasing with climate, sea level, and human impacts. Remote Sensing (RS) and Geographic Information Systems (GIS) provide critical information to rapidly and precisely monitor environmental changes in coastal areas and to understand and respond to environmental, economic, and social impacts. This study was aimed at determining the temporal changes in the coastline of the Seyhan Basin, which is one of the basins significantly affected by climate change and drought in Turkiye. In this context, approximately 50 km of coastline was automatically extracted on the Google Earth Engine (GEE) platform using Landsat satellite images from 1985\u0026ndash;2023. This coastline was divided into 3 different regions, and spatial analysis was performed with different statistical proportioning techniques (EPR, LRR, NSM, SCE, and WLR) according to years with the Digital Shoreline Analysis System (DSAS) tool. In addition, to determine whether there is a statistically significant difference between the results obtained from the different methods used to determine the coastal change, the Kruskal-Wallis H test and ANOVA test were applied by min-max normalization. The amounts of erosion and deposition found according to different methods vary by region. Statistical differences were found between the methods used, varying by region. In general, NSM and EPR methods provided similar results in determining coastal changes, while other methods differed by region. In the study, the Kalman filtering model was also used to predict the coastline for the years 2033 and 2043 and to identify areas that are vulnerable to erosion and deposition on the future coastline. Comparisons were made to determine the performance of Kalman filtering. In the 10-year and 20-year future forecasts for determining the coastline for the years 2033 and 2043 with the Kalman filtering model, it was determined that the excessive prediction time negatively affected the performance in determining the coastal boundary changes.\u003c/p\u003e","manuscriptTitle":"Forecasting future scenarios of coastline changes in Turkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:11:46","doi":"10.21203/rs.3.rs-4411235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-24T11:31:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T17:45:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T23:58:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233442572038620231997121912380725364824","date":"2024-06-01T06:33:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113606233418638969600304959663595933038","date":"2024-06-01T04:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225863488696700597145394059723580865401","date":"2024-06-01T01:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54727405670063811152603165145836597043","date":"2024-05-31T15:11:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-31T13:53:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-31T13:51:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T08:07:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth Science Informatics","date":"2024-05-13T07:01:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-science-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esin","sideBox":"Learn more about [Earth Science Informatics](http://link.springer.com/journal/12145)","snPcode":"12145","submissionUrl":"https://submission.nature.com/new-submission/12145/3","title":"Earth Science Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d7ec1537-7047-4f22-94a4-883323a28495","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T15:59:34+00:00","versionOfRecord":{"articleIdentity":"rs-4411235","link":"https://doi.org/10.1007/s12145-024-01445-w","journal":{"identity":"earth-science-informatics","isVorOnly":false,"title":"Earth Science Informatics"},"publishedOn":"2024-08-21 15:57:06","publishedOnDateReadable":"August 21st, 2024"},"versionCreatedAt":"2024-06-07 23:11:46","video":"","vorDoi":"10.1007/s12145-024-01445-w","vorDoiUrl":"https://doi.org/10.1007/s12145-024-01445-w","workflowStages":[]},"version":"v1","identity":"rs-4411235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4411235","identity":"rs-4411235","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}