Utilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey

Utilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey | 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 Utilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey Muhittin Ozan Karaman, Saye Nihan ÇABUK, Emrah PEKKAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1127725/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Geographical information systems (GIS) facilitates both current landslide mapping processes and prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were developed in order to reduce the damages of possible landslides in Karaburun Peninsula of İzmir province. To fulfil this aim, landslide inventory map was produced from related databases in the first place followed by the development of parameter (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines) maps. Frequency ratio method was utilized for developing the landslide susceptibility maps and Reciever Receiver Operating Characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 36% and 52% of the study area had high and medium risk categories, respectively. 10% of the region has low landslide risk. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been formerly declared as a special protection area, and is a popular destination for both tourism activities and energy facilities. Karaburun Peninsula GIS Landslide Landslide suspectibility mapping Frequency ratio Parameter maps Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction The dynamic internal structure of the Earth has been through a continuous change, which is influenced by both natural and anthropogenic factors, since its creation. These natural factors commonly cause a variety of natural phenomena such as earthquakes, volcanic eruptions, floods, landslides, extreme weather events etc., while anthropogenic activities usually disrupt natural balances and turn natural events into disasters causing deaths and destructions (Alcantara-Ayala 2002 ). From this point, comprehensive disaster analyses and determination of natural hazards and risks are of critical significance to mitigate the potential losses especially in areas intensely inhabitated. Landslides and rockfalls are amongst the main natural events that may pose high risks to the humans and settelements. A diversity of factors such as geological, geomorphological, climatic and meteorological influences, as well as the human activities initiate landslides. There are also triggering factors which cause the gravity-driven downslope movement of the large masses (soil, rock, debris etc.) (Ercanoğlu et al. 2008 ). Landslides occur in a wide range of different geographies in the world and significantly affect the landscapes (Gariano and Guzetti 2016). Yet, while changing the physical structure of the environment, landslides can also end up with severe economic losses, infrastructure damages, injuries and fatalities (Prakash et al. 2020 ). According to UNISDR, 4.8 million people were affected from landslides worldwide and 18414 fatalities were recorded between 1998-2017 (United Nations Office for Disaster Risk Recudtion [UNISDR] 2017). A number of studies about natural disasters covering different research periods in Turkey also point out that landslides take the first place in terms of event number in the country. According to the evaluation based on the number of the influenced housing, landslides rank second after earthquakes in terms of the losses they caused (Çan et al. 2013 ). Landslides are very commonly observed in the Black Sea, Central and Eastern Anatolia regions in the country and result in severe physical and economic impacts as well as deaths and injuries. According to the Disaster and Emergency Management Presidency (AFAD), 13494 lanslides and 2596 rock fall events were reported in the country between 1965 and 2015, while 151 events happened in 2018 (AFAD 2015 ). The 2019 disaster statistics, on the other hand revelaed that 245 landslide/rock fall events took place in Turkey (AFAD 2020 ). Within this context, development of landslide susceptibility maps, which deals with the spatial likelihood of the mass movements associated with their occurrence in a particular area (Nsengiyumva and Valentino, 2020 ), is vital to make efficient physical planning, manage potential risks for the existing settlements and structures, construction works (dams, roads, etc.), and delicate landscapes. Thus, loss and damage risks are properly and timely mitigated (Highland and Bobrowsky 2008 ). For this reason, researches and studies focusing on landslides hazard and risk modelling via different approaches and techniques have been an important research area. Statistical methods (Mersha and Meten 2020 ; Pasang and Kubíček 2020 ; Thanh et al. 2020 ; Zhang et al. 2020 ), machine learning algorithms (Bui et al. 2020 ; Fang et al. 2021 ; Merghadi et al. 2020 ; Sahin 2020 ; Wang et al. 2020 ) and hybrid models (Chen and Chen 2021 ; Chen and Li 2020 ) are commonly utilized by a good number of researchers to develop susceptibility maps, make predictions for potential flows and compare the efficiency and accuracy of different techniques. For example, Kirshbaum et al. (2020) examined the landslides in the High Mountain Asia region mostly initiated by extreme precipitation, and used satellite and Global Climate Model data and applied Landslide Hazard Assessment for Situational Awareness (LHASA) model to determine the potential landslide hazard in the future within the study area. Slope, lithology, land cover change, distance to road networks, and distance to fault zones data were utilized for the development of the landslide susceptibility map. Lui et al. (2021) utilized three machine learning techniques to model the landslide susceptibility triggered by rainfall in Veikledalen Valley, Norway. The authors used slope angle, aspect, plan curvature, profile curvature, flow accumulation, flow direction, distance to rivers, total water content, saturation, rainfall and distance to roads data as the triggering factors. Lacroix et al. ( 2020 ) aimed to determine the relation between the irrigation and the landslide activities on the southwestern coats of Peru. The authors used Hexagon spy satellite and SPOT 6/7 images to detect the land use and morphological changes (elevation change patterns) between 1978 and 2016, and benefited from KH9, Landsat 5 and Landsat 8 imagery to determine the horizontal displacements in the study area. The results showed that large slow-moving landslides occurred within the irrigated areas. Lee and Pradhan ( 2007 ) used parameters such as slope, aspect, curvature, precipitation distribution, lithology, vegetation index, land cover, distance from drainage, etc. and applied both frequency ratio (FR) and logistic regression (LR) methods to develop landslide hazard map in Selangor, Malaysia, which is prone to severe landslide activities especially triggered by intense rainfall. In their study, the authors both used satellite images and conducted field surveys to detect the landslide locations, and after the analyses concluded that FR method provided more accurate results compared to LG in their study area. Besides the techniques adopted, the parameters used for assessing the landslide hazards are of vital importance and may vary according to the aim of the study and the characteristics of the geographic context. Gökçeoğlu and Ercanoğlu ( 2001 ) focused on the commonly used geological, geomorpholological, hydrological, and antropogenic data sets and evaluated 21 studies conducted for the development of landslide susceptibility maps. The authors found that slope data was used in all 21 studies, lithology in 20, distance to main faults in 11, curvature and elevation in 10, and drainage network, vegetation and land use potential datasets in 8 studies. In a nother similar study, 117 studies in the literature were examined to determine the parameters used to develop landslide susceptibility maps. According to the results, it was detected that 94.02% of the studies have used curvature, 67.52% lithology, 63.25% aspect, 51.28% drainage characteristics, and 50.43% elevation (AFAD 2015 ). Within this context, the aim of this study is to develop a GIS-supported landslide susceptibility maps in Karaburun Peninsula, İzmir, using FR method. As Thanh et al. ( 2020 ) also underlined, FR is a very popular bivariate statistical method for the assessment of the landslide susceptibility, since it is easy to use and provide good results. Reciever Operating Characteristic (ROC) analysis was performed for the determination of accuracy of the landslide susceptibility map. 2. Material And Methods 2.1. Study Area The study area is Karaburun Peninsula located between 26° 21′–26° 38′ N longitudes and 38°25′–38° 40′ E latitudes to the west of İzmir province, which is the third biggest city in Turkey (Fig. 1 ). Karaburun has a coastal length of 130 km (Isik Pekkan et al. 2021 ), covers a surface area of 420 km 2 and extends to the Aegean Sea (Prefecture of Karaburun 2019 ). The annual average temperature in the study area varies between 15-20 C o and the annual average precipitation is around 650-700 mm. The peninsula is an important area for a good number of wind farm projects, since the wind speed reaches to 50 km/hours in the region (Prefecture of Karaburun 2019 ). The terrain of the peninsula is quite rugged and the highest spot in the region is Akdağ mountain with an elevation of 1212 m. Mountains of limestone and andesite surround the sea-level plains and tectonic pits (İzmir Kalkınma Ajansı (İZKA) 2013; Kalafatçıoğlu 1961 ). This topographic structure results in the formation of a diversity of attractive bays along the coastal areas of Karaburun, which makes the area a popular tourist destination. Road, tourism facility, wind farm construction activities have therefore become very intensive for the last 10 years. The low-density population in the region increases especially during the tourism seasons (Prefecture of Karaburun 2019 ). Karaburun is also a very significant area in terms of its unique landscape characteristics and hosts marine, coastal, mountain, forest and wetland ecosystems (Isik Pekkan et al. 2021 ). Accordingly, the region was declared Special Environment Protection Site on 15.03.2019 by the Ministry of Environment and Urbanization. Considering both this new conservation status of the peninsula and the delicate characteristics and increasing demand in the area for tourism and wind farm establishments, it has become an essential requirement to conduct more comprehensive researches in the study area. From this perspective, disaster based studies, risk and hazard determination works are also important in the area, so that proper planning and implementation works can be made to protect the existing characteristics and mitigate the potential negative impacts on the ongoing and planned activities. The landslide susceptibility maps are also necessary to determine the landslide hazards in the region 2.2. Material and Data The main material used in this study is the spatial data of the Karaburun Peninsula. ArcGIS, Microsoft Excel and IBM SPSS Statistics software were used for data processing, analyses and visualitation processes. The spatial data of the study were transformed into Shape file (*.shp) format. Table 1 summarizes the spatial data and the type of the spatial analysis used as well as the parameter maps developed within the aim of the study Table 1 Spatial data of the study Parameter Map Name of Data Type of Data Original Name Type of Analysis Landslide Inventory Landslide Vector Landslide Classification Elevation, slope, aspect, curvature Digital Elevation Model Raster ASTER Global Digital Elevation Model V003 / 30 m spatial resolution Topographic analysis Land use Land use map Raster Corine Land Cover Change /100 m spatial resolution Classification Lithology Lithology Vector Geologial formation Classificaiton Distance to roads Distance to roads Raster Road Proximity analysis and classification Distance to rivers Distance to rivers Raster ASTER Global Digital Elevation Model V003 / 30 m spatial resolution Hydrological and proximity anaylsis, classification Distance to fault lines Distance to fault lines Raster Fault Proximity analysis and classification Vegatation cover Vegetation cover Raster Landsat 8 TM / 30 m spatial resolution NDVI index, classsification The primary data of this study is the existing landslide areas (landslide inventory), which was obtained from the earthsciences portal of General Directorate of Mineral Research and Exploration (MTA) and used for the production of statistical data from particular parameters which initiate the landslides. Since only 3 landslide areas exist in Karaburun Peninsula according to the records, all 33 former landslide areas within İzmir province were used to obtain significant statistical results. Still, the amount of existing landslide areas is considered low compared to the size of the study area, especially when the approaches in other similar studies are exmained (Basharad 2016; Chen 2014; Chen 2016; Akgün et al. 2008 ). 2.3. Methods This study is based on the development of landslide susceptibility maps of Karaburun Peninsula, Izmir province, using method namely FR .Weighted parameter maps were overlaid via GIS capabilities to produce the landslide susceptibility map of the study area. Fig. 2 summarizes the workflow of the study. For the development of the parameters maps in the study area various methods were used. NDVI technique was applied to determine the vegetation cover (Esendal et al. 2018). Buffer analysis was performed for the determination of the buffer zones around linear fetaures such as roads, rivers and fault lines. Topographic analysis were made to produce elevation, slope and slope maps. Land use parameter map was produced by the reclassification and organization of the CORINE map of the study area. Table 1 also summarizes the main methods utilized for the development of the parameter maps. Detailed information related to parameter map development and FR method utilization processes are provided in the following sections. 2.3.1. Development of Parameter Maps Although landslide susceptibility map development studies have become widespread, there is still no consensus on the determination of the necessary parameters and the methods (Gökçeoğlu and Ercanoğlu 2001 ). Therefore, previous studies were examined and referred for the determination of the parameters (Akgün et al. 2008 ; Chen et al. 2014 ; Gökçekoğlu et al. 2001; Huang and Zhao 2018 ; Kelarestaghi and Ahmadi 2009 ; Özdemir and Altural 2013 ; Pachauri and Pand 1992; Sarkar and Kanungo 2017 ; Youssef et al. 2015 ). The parameters for this study was determined as elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines. Parameter data were obtained from various resources and classified in the first place to statistically evaluate the relation between these data and the characteristics of the existing landslide within the study area to produce the landslide susceptibility map. The resolution was resampled to 30 meters. The existing landslide areas located to the north of the study area around Çandarlı Bay region, which cover a considerably large surface, were extracred from the process due to their possible negative influence on the results of the statistical analyses. Therefore, a total of 33 previous landslide areas were used for the study. After the parameter maps were developed, they were reclassified in accordance with the sublayers in the maps based on the information in the literature. Each reclassified parameter map was masked with the polygons comprising the 33 landslide areas in the study area and the number of the pixels in each masked area was calculated to determine the frequency values. 2.3.2. Frequency Ratio Method (FR) FR method provides objective weighting of the parameter maps together with their sublayers in determining the landslide susceptibility areas and is based on the principle of computing the observation frequency of parameter sublayers in landslide areas. FR method enables to associate the sublayers of the parameter maps in the study area with the ones that of existing landslide areas. FR method runs on a probability model that defines the likelihood of an event happening (Ataol and Yeşilyurt 2014 , Pham, et al. 2015 , Soyoung, et al. 2013, Silalahi, et al. 2019 ). The formula used for FR method is given below; $$FR = PLO \backslash PIF$$ 1 where \(PLO\) represents the percentage of the presence of landslides within each sublayer that affect the landslide, and \(PIF\) is the percentage of each sublayer affecting the landslide in the parameter map. The FR calculated via this formula is used for assigning the weights for parameter maps (Erener and Lacasse 2007 ; Demir 2018 ). Landslide susceptibility map is developed by the addition of the weighted parameter maps, in which lower values show the low landslide susceptibility, while high ones correspond to the high landslide susceptibility. These values are reclassified with equal intervals to obtain the final state of the landslide susceptibility map. Regardingly, in this study, the number of pixels corresponding to the landslide area of each sublayer in the relevant parameter map was determined and then the FRs of the sublayers in the landslide areas were calculated using this value. Receiver Operating Characteristic (ROC) analysis was used for the determination of the accuracy of the landslide susceptibility maps produced with FR. ROC analysis is one of the methods used to organize the classifications obtained by statistical processes and to evaluate their performance. Frequently used for medicine bioinformatics, finance and GIS studies, ROC analysis provides the comparison of the statistical models with the real data. When the area below the curve is close to 1, the specificity of the model increases (Mas et al. 2013 , Fawcett 2006 ). 3. Results 3.1. Parameter and Landsclide Susceptibility Maps In accordance with the methodology explained in the relevant sections, 10 parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines) were developed in the first place for Karaburun Peninsula. The parameter maps were used for the development of the landslide susceptibility map of the study area. Fig. 4 - 13 illustrate the parameter maps of the study and Fig. 14 shows the final map (landslide susceptibility map) developed with FR method. Table 2 Frequency table Parameter Classification Value Frequency Value Elevation 0-100 8.5 100-200 27.6 200-300 95.1 300-400 83.5 400-500 100.0 500-600 75.1 600-700 11.2 700-800 6.4 800-900 0.7 900-1000 3.1 1000-1100 2.7 1100-1200 5.5 1200-1300 6.9 1300-1400 16.4 1400-1500 34.1 1500-1600 86.5 1600-1700 34.0 1700-1800 0.0 1800-1900 0.0 2000-2100 0.0 2100-2200 0.0 2200-2300 0.0 Aspect North 6.3 Northeast 0.1 East 100.0 Southeast 63.1 South 42.1 Southwest 46.6 West 49.9 Nortwest 51.1 Slope 0-10 90.4 10-20 100.0 20-30 52.2 30-40 41.7 40-50 37.7 50-60 0.0 60-70 0.0 70-80 0.0 Curvature (-29)-(-1) 68.7 (-1)-(-0.5) 100.0 (-0.5)-0 93.4 0-0.5 95.8 0.5-1 93.9 1-27 61.6 Land use Urban Areas 35.9 Transport 48.9 Construction and Mining Areas 0.0 Agricultural Areas 18.3 Forests 3.8 Grasslands 100.0 Coastal Areas 0.0 Bareen Rocky Lands 0.0 Water Resources 1.0 Vegetation cover Water Body 49.1 Urban Area, Semi Desert 5.8 Urban Area, Dry Soil, Clay Surface 7.3 Moist Soil, Bare Soil 46.9 Forest, Grassland 100.0 Forest, Farmland 41.2 Dense Vegetation 8.3 Lithology Quatermary 2.2 Neogene 0.3 Paleogene 100.0 Mesozoic 1.5 Paleozoic 1.0 Precambrian 5.9 Distance to roads 0-100 70.9 100-200 81.0 200-300 86.1 300-400 83.2 < 400 100.0 Distance to rivers 0-100 43.9 100-200 60.3 200-300 68.0 300-400 67.5 < 400 100.0 Distance to fault lnes 0-50 99.4 50-100 94.5 100-150 100.0 150-200 99.5 200-250 95.7 250-300 97.0 300-350 93.3 350-400 88.4 400-450 88.9 450-500 80.6 < 500 43.4 The table above shows the calculated frequency values. A susceptibility map was produced using these values in the Karaburun Peninsula. When the frequency values were examined, it was seen that the frequencies were evenly distributed or concentrated in a single class in some parameters. This reduces the effect of these parameters on the susceptibility map. Field studies on each of these parameters or other studies to increase accuracy are required. According to the landslide susceptibility map (Figure 14 ) of İzmir province and Karaburun Peninsula, 10% of the study area falls into low risk category and 50% into medium risk category. 40% and 50% of the existing landslide areas comprise medium and high risk zones, respectively. The results show that Karaburun peninsula is mostly made up of high (36%) and medium (52%) risk categories. Still, 10% of the region has low risk characteristics in terms of landslide susceptibility. Higher elevations within the peninsula have medium landslide susceptibility. Karaburun and Mordoğan districts are mainly made up of medium susceptibility areas. 3.2. Accuracy Results In a good number of studies ROC method have been used for the determination of the accuracy of the landslide susceptibility maps. In this study, ROC analysis, which is particularly used in biostatistics for the evaluation of performance of the diagnosis and the test was adopted. In ROC, the area below the curve shows the quality of the relation between the test and the diagnoses. The area below the curve line is between 0.5 and 1, where 1 refers to a perfect specificity and 0.5 random specificity (Ayalew and Yamagishi 2005 ; Reyhanlıoğlu Keçeoğlu et al. 2016). Figure 15 illustrates the ROC results of the landslide susceptibility maps of the study area. Table 3 summarizes the information related to the ROC analyses. Table 3 Information realted to ROC curve Area Asymptotic Sig. b Asymptotic 95% Confidence Interval Lower Bound Upper Bound Landslide susceptibility map (FR method) 0.783 0 0.777 0.788 According to ROC analysis, the accuracy of the landslide susceptibility map developed with FR method was found 78.3%. 4. Conclusions In this study, FR method was utilized for the production of the landslide susceptibility map, and thus to spatially predict the landslide risks in Karaburun Peninsula, İzmir province. The study area is a delicate region in terms of landscape characteristics and a popular tourist destination. Landslide susceptibility map development efforts are based on the analysis of natural, environmental and triggering parameters by using different statistical methods. In the study, most common 10 parameters (elevation, aspect, slope, curvature, lithology, land use, vegetation cover, distance to the roads, distance to fault lines and distance to the rivers), which were frequently used in previous studies, were analyzed using FR method. The data and the methods of the study were determined in accordance with the literature review. All the parameter maps were organized to have the same standards in terms of study area boundaries, type, scale, raster resolution and coordinate system. In addition, experts were consulted to determine the relation between the landslides and the triggering parameters. Regarding the process and the produced landslide susceptibility map, the followings were concluded. In FR method, the influence of “distance to rivers” parameter was found considerably low. This is thought to be mostly because the winter flows are very common in the region. The streams in the region are generally dry for most of the year. Increase in the pore water pressure is one of the reasons that cause landslides. This is assumed to be the reason for the landslides in the study area to occur mainly in the coastal areas. According to the MTA database, only 3 former landslides were recorded in Karaburun Peninsula. Due to this low number, all the existing landslide data for whole İzmir province were used to obtain statistically significant results. On the other hand, AFAD also keeps the inventory of the landslides in the country. From this point, it is necessary to use the data of both institutions to obtain more accurate results. Field surveys, satelleite imagery, UAV and LIDAR applications are also utilized for the determination of the landslides. Thefore, it is essential to update, integrate and enrich the exsting landslide inventory with the support of the advanced technologies. Thus, it will be possible for researches to develop more accurate and precise maps. A good number of wind farm establishments exists within the study area, mostly erected on the higher elevations. Accordingly, many roads have been built to provide transportation to the wind farms. However, the road data used in this study excludes these mentioned roads, which in fact may be important triggering factors and should be obtained and analysed for further works. The authors believe that these ignored roads are likely to increase the landslide susceptibility in the region. The landslide susceptibility map revealed that the Karaburun Peninsula mostly comprised medium risk lands. In this study yhe number of landslide inventory is rather low compared to similar studies. Srill, it is of significance to allow for the landslide risks for strategic and physical planning processes in the region as well as a range of economic, social and environmental works. Declarations Ethics approval Not applicable Consent to Participate Not applicable Consent to Publish Not applicable Author Contributions All authors contributed to the study conception and design. Conceptualization, data collection, analysis, and first draft writing stages were performed by Muhittin Ozan KARAMAN. Saye Nihan ÇABUK and Emrah PEKKAN supervised and contributed to the context and medthod development, evaluation of the results, and final writing. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Availability of data and materials Not applicable References AFAD (2008) Türkiye Heyelan yoğunluk Haritası. https://www.afad.gov.tr/kurumlar/afad.gov.tr/3506/xfiles/96-2014060215311-heyelan_yogunluk_a1_olceksiz.pdf. Accessed 10 June 2019 AFAD (2015). Bütünleşik Tehlike Haritalarının Hazırlanması Heyelan ve Kaya Düşmesi Pratik Kılavuz. AFAD, Ankara AFAD (2020) Afet Yönetimi Kapsamında 2019 Yılına Bakış ve Doğa Kaynaklı Olay İstatistikleri. https://www.afad.gov.tr/kurumlar/afad.gov.tr/e_Kutuphane/Kurumsal-Raporlar/Afet_Istatistikleri_2020_web.pdf. Accessed 10 June 2019 Akgün A, Dağ S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54 (6):1127-1143. https://doi.org/ 10.1007/s00254-007-0882-8 Alcantara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphol 47(2-4):107-124. https://doi.org/ 10.1016/S0169-555X(02)00083-1 Ataol M, Yeşilyurt S (2014) Çankırı-Ankara Karayolu Boyunca (Akyurt-Çankırı Arası) Heyelan Risk Bölgelerinin Belirlenmesi . Cograf Derg 29:51-69 Ayalew L, Yamagishi H (2005) The application og GIS-based logistic regression for landslide suspectibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphol 65(1-2):15-31. https://doi.org/10.1016/j.geomorph.2004.06.010 Basharad M, Shah H, Hameed N (2016) Lanslide Suspectibility Mapping Using GIS and Weighted Overlay Method: A Case Study from NW Himayalas, Pakistan. Arab J Geosci 9:292(2016). https://doi.org/ 10.1007/s12517-016-2308-y Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426. https://doi.org/10.1016/j.catena.2019.104426 Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016) A GIS-Based Comparative Study of Frequency Ratio, Statistical Index and Weights –of-Evidence Models in Lanslide Suspectibility Mapping. Arab J Geosci 9:204. https://doi.org/ 10.1007/s12517-015-2150-7 Chen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014). Lanslide Suspectibility Mapping Based on GIS and Information Value Model for the Chencang District og Bajoi, China. Arab J Geosci 7:4499-4511. https://doi.org/10.10007/s12517-014-1369-z Chen, X., & Chen, W. (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena 196:104833. https://doi.org/10.1016/j.catena.2020.104833 Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195:104777. https://doi.org/ 10.1016/j.catena.2020.104777 Çan T, Duman T, Olgun Ş, Çörekçioğlu Ş, Gülmez F, Elmacı H, Hamzaçebi S, Emre Ö (2013). Türkiye Heyelan Veri Tabanı. https://www.hkmo.org.tr/resimler/ekler/85a47f65233d5d0_ek.pdf . Accessed 20 June 2021 Demir G (2018) Coğrafi Bilgi Sistemleri ile Suşehri (Sivas) Heyelan Duyarlılık Analizi. GUSTIJ 8:96-112. https://doi.org/10.17714/gumusfenbil.299987 Ercanoğlu M, Hasekioğulları G, Günal B (2008) Heyelan Duyarlılığı Çalışmalarında Türkiye'nin Uluslararası Bilimsel Literatürdeki Yeri. Bull Eng Geol 26-27:35-51 Erener A, Lacasse S (2007) Landslide susceptibility mapping using GIS. In TMMOB Chamber of Survey and Cadastre Engineers National Geographic Information Systems Congress. Trabzon,Turkey. Esendal Bozkurt N, Zontul M, Aslan Z (2018) Uydu Verilerine Dayalı Olarak Bitki Örtüsü Analizi. Aurum J Eng Syst Archit 2(1):75-82 Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321-347. https://doi.org/10.1080/13658816.2020.1808897 Fawcett T, (2006) An Introduction to ROC Analysis. Pattern Recognit Lett 27-8:861-874 Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227252 Gökçeoğlu C, Ercanoğlu M (2001) Heyelan Duyarlılık Haritalarının Hazırlanmasında Kullanılan Parametreleri İlişkin Belirsizlikler. Bull Earth Sci 23:189-206. Highland LM, Bobrowsky P (2008) The landslide handbook—A guide to understanding landslides https://pubs.usgs.gov/circ/1325/pdf/Sections/Section1.pdf . Accessed 20 June 2021 Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. Catena 165:520–529. https://doi.org/ 10.1016/j.catena.2018.03.003 İZKA (2013) Urla-Çeşme-Karaburun Yarımadası Sürdürülebilir Kalkınma Stratejisi http://izka.org.tr/wp-content/uploads/pdf/14_yarimada_kalkinma_stratejisi.pdf . Accessed 20 June 2021 Isik Pekkan O, Kurkcuoglu MAS, Cabuk SN, Aksoy T, Yilmazel B, Kucukpehlivan T, Dabanli A, Cetin M (2021) Assessing the effects of wind farms on soil organic carbon. Environ Sci Pollut Res 28:18216-18233. https://doi.org/10.1007/s11356-020-11777-x Kalafatçıoğlu A (1961) A Geological Study in the Karaburun Peninsula . Bull Miner Res Explor 56 (56):40-49 Keçecioğlu Ç, Gelbal S, Doğan N (2016) ROC Eğrisi ile Kesme Puanının Belirlenmesi. J Soc Sci 50:553-562. https://doi.org/ 10.9761/JASSS3564 Kelarestaghi A, Ahmadi H (2009) Landslide suspectibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95-101. https://doi.org/ 10.1007/s12517-008-0022-0 Kirschbaum D, Kapnick SB, Stanley T, Pascale S (2020) Changes in extreme precipitation and landslides over High Mountain Asia. Geophys Res Lett 47: e2019GL085347. https://doi.org/10.1029/2019GL085347 Lacroix P, Dehecq A, Taipe E (2020) Irrigation-triggered landslides in a Peruvian desert caused by modern intensive farming. Nat Geosci 13(1):56-60. https://doi.org/10.1038/s41561-019-0500-x Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33-41. https://doi.org/ 10.1007/s10346-006-0047-y Liu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, Lacasse S (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front 12(1):385-393. https://doi.org/ 10.1016/j.gsf.2020.04.014 Mas J, Filho B, Pontius R, Gutierrez M, Rodriques H (2013) A Suite of Tools for ROC Analysis of Spatial Models. ISPRS Int. J. Geo-Inf 2:869-887. https://doi.org/10.3390/ijgi2030869 Mersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7:20. https://doi.org/10.1186/s40677-020-00155-x Merghadi A, Yunus AP, Dou Jie, Whiteley Jim, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide suspectibility studies: A comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225 Nsengiyumva JB, Valentino R (2020) Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment. Geomat Nat Hazards Risk, 11(1):1250-1277. https://doi.org/10.1080/19475705.2020.1785555 Özdemir A, Altural T (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180-197. https://doi.org/10.1016/j.jseaes.2012.12.014 Pachauri A, Pant M (1992). Landslide Hazard Mapping Based on Geological Attributes. Eng Geol 32 (1-2):81-100. https://doi.org/10.1016/0013-7952(92)90020-Y Pham BT, Dieu TB, Prakash I, Dholakia MB (2015). Landslide Suspectibility Assessment at a Part of Uttarakhand Himalaya, India using GIS-based Statistical Approach of Frequency Method. Int J Eng Res Technol 4(11):338-344. http://dx.doi.org/10.17577/IJERTV4IS110285 Prefecture of Karaburun (2019) http://www.karaburun.gov.tr. Accessed 10 July 2019 Soyoung P, Chuluong C, Byungwoo K, Jinsoo K (2012) Landslide Susceptibility Mapping Using Frequency Ratio, Analytic Hierarchy Process, Logistic Regression and Artificial Neural Network Methods at the Inje Area, Korea. Env Earth Sci 68(5):1443-1464. https://doi.org/ 10.1007/s12665-012-1842-5 Pasang S, Kubíček P (2020) Landslide susceptibility mapping using statistical methods along the Asian Highway, Bhutan. Geosci 10(11):430. https://doi.org/10.3390/geosciences10110430 Prakash N, Manconi A, Loew S (2020) Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens 12(3) :346. https://doi.org/10.3390/rs12030346 Sahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci 2(7):1-17. https://doi.org/10.1007/s42452-020-3060-1 Sarkar S, Kanungo DP (2017) GIS Application in Landslide Susceptibility Mapping of Indian Himalayas. In: Yamagishi H, Bhandary NP (ed) GIS Landslide. Springer, Tokyo, pp 211–219. https://doi.org/ 10.1007/978-4-431-54391-6_12 Silalahi FES, Pamela Arifianti Y, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci. Lett. 6:10. https://doi.org/10.1186/s40562-019-0140-4 Thanh DQ, Nguyen DH, Prakash I, Jaafari A, Nguyen VT, Van Phong T, Pham BT (2020). GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong, Vietnam Vietnam J Earth Sci 42(1):55-56. https://doi.org/10.15625/0866-7187/42/1/14758 UNISDR (2018) Economic Losses, Poverty and Disasters: 1998-2017. https://www.preventionweb.net/files/61119_credeconomiclosses.pdf . Accessed 28 August 2021 Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers Geosci 138:104445. https://doi.org/ 10.1016/j.cageo.2020.104445 Zhang YX, Lan HX, Li LP, Wu YM, Chen JH, Tian NM (2020) Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China. J Mt Sci 17(2):340-357. https://doi.org/10.1007/s11629-019-5702-6 Youssef AM, Al-Kathery M, Pradhan B (2015). Landslide suspectibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19:113-134. https://doi.org/10.1007/s12303-014-0032-8 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 21 Apr, 2022 Reviews received at journal 12 Jan, 2022 Reviewers invited by journal 10 Jan, 2022 Editor assigned by journal 14 Dec, 2021 First submitted to journal 30 Nov, 2021 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1127725","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":75521954,"identity":"ad5c392e-16e3-49aa-aa8b-332cc4586bff","order_by":0,"name":"Muhittin Ozan Karaman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYLCCBAMJZn4wo4BYLR8KbNglG8B6idTBOONDGr/BARCTGC38/IefPeYxOCxtfH514ocHBgzy/GIH8GuRbDhmbgzUYmx24+1mCaDDDGfOTsCvxeBgg5k0UEuy2Y2zG0BaEgxuE9JymP0bSEv95hlnN/8gTssxHjPJGQZpzAb8vduIs0Wyh6dM4oOBDbPEDd5tFsAIIuwXfv7jQMP/AKOy/+zmmz8qbOT5pQloQQAJsEoJYpWD7TtAiupRMApGwSgYSQAA8CxBbAQWIB8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9835-5923","institution":"Eskişehir Technical University: Eskisehir Teknik Universitesi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Muhittin","middleName":"Ozan","lastName":"Karaman","suffix":""},{"id":75521955,"identity":"3dccc10f-6b93-4107-91ae-e156be89c0c3","order_by":1,"name":"Saye Nihan ÇABUK","email":"","orcid":"","institution":"Eskişehir Technical University: Eskisehir Teknik Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saye","middleName":"Nihan","lastName":"ÇABUK","suffix":""},{"id":75521956,"identity":"34109501-99a3-491a-a3b4-c58f410f9798","order_by":2,"name":"Emrah PEKKAN","email":"","orcid":"","institution":"Eskişehir Technical University: Eskisehir Teknik Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emrah","middleName":"","lastName":"PEKKAN","suffix":""}],"badges":[],"createdAt":"2021-11-30 12:34:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1127725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1127725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17255020,"identity":"da6f8414-ba8d-4821-b265-139d426449fc","added_by":"auto","created_at":"2022-01-12 16:56:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324907,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/396d868522dd90cce30e0b61.jpeg"},{"id":17255019,"identity":"067f6feb-1443-429f-840b-68217d5d61cb","added_by":"auto","created_at":"2022-01-12 16:56:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2903446,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the study\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/b29eeddb53d8441e8e2e0667.jpeg"},{"id":17255219,"identity":"169c2a84-0de9-4070-808f-264bb841b07e","added_by":"auto","created_at":"2022-01-12 16:59:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":705512,"visible":true,"origin":"","legend":"\u003cp\u003eLanslide inventory map\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/591a71e84078e0678dd3492e.png"},{"id":17254878,"identity":"6b1d9cc9-42a2-45cc-8d73-71c95ef64a0a","added_by":"auto","created_at":"2022-01-12 16:53:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":709910,"visible":true,"origin":"","legend":"\u003cp\u003eElevation map\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/a34ade976cafe9bdd7101300.png"},{"id":17254539,"identity":"318fc579-d73f-4313-8ab0-ffa831a326da","added_by":"auto","created_at":"2022-01-12 16:47:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":699981,"visible":true,"origin":"","legend":"\u003cp\u003eAspect map\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/69457160860998ac45964358.png"},{"id":17254553,"identity":"85b98b04-0ef7-45d7-9261-a052941a84f4","added_by":"auto","created_at":"2022-01-12 16:47:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":673204,"visible":true,"origin":"","legend":"\u003cp\u003eSlope map\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/7268620a05d54e78cab6eaa4.png"},{"id":17254544,"identity":"7638ee5c-050f-4e9f-84f8-5df035989d56","added_by":"auto","created_at":"2022-01-12 16:47:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":688118,"visible":true,"origin":"","legend":"\u003cp\u003eCurvature map\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/9bb846274564dca699dabeca.png"},{"id":17254613,"identity":"d519742f-6b0b-4620-b74a-572c819402d3","added_by":"auto","created_at":"2022-01-12 16:50:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":700178,"visible":true,"origin":"","legend":"\u003cp\u003eLand use map\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/124abcc3d01bc4a229ad85ee.png"},{"id":17254616,"identity":"11d07a3a-931a-4575-af17-d1ce48958575","added_by":"auto","created_at":"2022-01-12 16:50:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":712939,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation cover map\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/772224f988780d9cc1e7ae62.png"},{"id":17254881,"identity":"c3514e83-ecfb-4d36-bc0e-2dfeaf6dd519","added_by":"auto","created_at":"2022-01-12 16:53:41","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":662931,"visible":true,"origin":"","legend":"\u003cp\u003eLithology map\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/5a2c4af0c6ffe4f4cdaeb6f1.png"},{"id":17254551,"identity":"c128e76d-04fc-4dad-b921-f659c42ed84f","added_by":"auto","created_at":"2022-01-12 16:47:42","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":645691,"visible":true,"origin":"","legend":"\u003cp\u003eDistance to roads map\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/db2f924a9c312861f4be147d.png"},{"id":17254883,"identity":"2e8c8c55-1312-4b5e-b26e-2a296a16660a","added_by":"auto","created_at":"2022-01-12 16:53:42","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":645691,"visible":true,"origin":"","legend":"\u003cp\u003eDistance to rivers map\u003c/p\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/ec441a6c00d32c1eadbda21d.png"},{"id":17254619,"identity":"c0af1ee1-6169-4e85-8acd-fffe862b9065","added_by":"auto","created_at":"2022-01-12 16:50:41","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":662083,"visible":true,"origin":"","legend":"\u003cp\u003eDistance to fault lines map\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/2a1b21ae347379ce29e36d33.png"},{"id":17254550,"identity":"b724d1e4-b6fb-4388-a70b-1ad1fa58d557","added_by":"auto","created_at":"2022-01-12 16:47:42","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":399515,"visible":true,"origin":"","legend":"\u003cp\u003eKaraburun Peninsula landslide susceptibility map developed with FR method\u003c/p\u003e\u003cp\u003e\t\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/486627f7505cb2cbcbd3c0c0.png"},{"id":17254548,"identity":"cb3b0e36-d481-47b2-b27d-d25fc36b1ded","added_by":"auto","created_at":"2022-01-12 16:47:41","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":20829,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for landslide susceptibility map\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/f8f3e1c907c424a9890921dc.png"},{"id":17255220,"identity":"c75e8916-f7a5-4e6b-85d6-4f83db085be4","added_by":"auto","created_at":"2022-01-12 16:59:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3632301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1127725/v1/e7e6b064-2c9b-4ed4-a767-b655e4a27303.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eUtilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe dynamic internal structure of the Earth has been through a continuous change, which is influenced by both natural and anthropogenic factors, since its creation. These natural factors commonly cause a variety of natural phenomena such as earthquakes, volcanic eruptions, floods, landslides, extreme weather events etc., while anthropogenic activities usually disrupt natural balances and turn natural events into disasters causing deaths and destructions (Alcantara-Ayala \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). From this point, comprehensive disaster analyses and determination of natural hazards and risks are of critical significance to mitigate the potential losses especially in areas intensely inhabitated.\u003c/p\u003e \u003cp\u003eLandslides and rockfalls are amongst the main natural events that may pose high risks to the humans and settelements. A diversity of factors such as geological, geomorphological, climatic and meteorological influences, as well as the human activities initiate landslides. There are also triggering factors which cause the gravity-driven downslope movement of the large masses (soil, rock, debris etc.) (Ercanoğlu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Landslides occur in a wide range of different geographies in the world and significantly affect the landscapes (Gariano and Guzetti 2016). Yet, while changing the physical structure of the environment, landslides can also end up with severe economic losses, infrastructure damages, injuries and fatalities (Prakash et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to UNISDR, 4.8 million people were affected from landslides worldwide and 18414 fatalities were recorded between 1998-2017 (United Nations Office for Disaster Risk Recudtion [UNISDR] 2017).\u003c/p\u003e \u003cp\u003eA number of studies about natural disasters covering different research periods in Turkey also point out that landslides take the first place in terms of event number in the country. According to the evaluation based on the number of the influenced housing, landslides rank second after earthquakes in terms of the losses they caused (\u0026Ccedil;an et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Landslides are very commonly observed in the Black Sea, Central and Eastern Anatolia regions in the country and result in severe physical and economic impacts as well as deaths and injuries. According to the Disaster and Emergency Management Presidency (AFAD), 13494 lanslides and 2596 rock fall events were reported in the country between 1965 and 2015, while 151 events happened in 2018 (AFAD \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The 2019 disaster statistics, on the other hand revelaed that 245 landslide/rock fall events took place in Turkey (AFAD \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this context, development of landslide susceptibility maps, which deals with the spatial likelihood of the mass movements associated with their occurrence in a particular area (Nsengiyumva and Valentino, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), is vital to make efficient physical planning, manage potential risks for the existing settlements and structures, construction works (dams, roads, etc.), and delicate landscapes. Thus, loss and damage risks are properly and timely mitigated (Highland and Bobrowsky \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For this reason, researches and studies focusing on landslides hazard and risk modelling via different approaches and techniques have been an important research area. Statistical methods (Mersha and Meten \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pasang and Kub\u0026iacute;ček \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thanh et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), machine learning algorithms (Bui et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Merghadi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sahin \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and hybrid models (Chen and Chen \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen and Li \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are commonly utilized by a good number of researchers to develop susceptibility maps, make predictions for potential flows and compare the efficiency and accuracy of different techniques.\u003c/p\u003e \u003cp\u003eFor example, Kirshbaum et al. (2020) examined the landslides in the High Mountain Asia region mostly initiated by extreme precipitation, and used satellite and Global Climate Model data and applied Landslide Hazard Assessment for Situational Awareness (LHASA) model to determine the potential landslide hazard in the future within the study area. Slope, lithology, land cover change, distance to road networks, and distance to fault zones data were utilized for the development of the landslide susceptibility map. Lui et al. (2021) utilized three machine learning techniques to model the landslide susceptibility triggered by rainfall in Veikledalen Valley, Norway. The authors used slope angle, aspect, plan curvature, profile curvature, flow accumulation, flow direction, distance to rivers, total water content, saturation, rainfall and distance to roads data as the triggering factors. Lacroix et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) aimed to determine the relation between the irrigation and the landslide activities on the southwestern coats of Peru. The authors used Hexagon spy satellite and SPOT 6/7 images to detect the land use and morphological changes (elevation change patterns) between 1978 and 2016, and benefited from KH9, Landsat 5 and Landsat 8 imagery to determine the horizontal displacements in the study area. The results showed that large slow-moving landslides occurred within the irrigated areas. Lee and Pradhan (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) used parameters such as slope, aspect, curvature, precipitation distribution, lithology, vegetation index, land cover, distance from drainage, etc. and applied both frequency ratio (FR) and logistic regression (LR) methods to develop landslide hazard map in Selangor, Malaysia, which is prone to severe landslide activities especially triggered by intense rainfall. In their study, the authors both used satellite images and conducted field surveys to detect the landslide locations, and after the analyses concluded that FR method provided more accurate results compared to LG in their study area.\u003c/p\u003e \u003cp\u003eBesides the techniques adopted, the parameters used for assessing the landslide hazards are of vital importance and may vary according to the aim of the study and the characteristics of the geographic context. G\u0026ouml;k\u0026ccedil;eoğlu and Ercanoğlu (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) focused on the commonly used geological, geomorpholological, hydrological, and antropogenic data sets and evaluated 21 studies conducted for the development of landslide susceptibility maps. The authors found that slope data was used in all 21 studies, lithology in 20, distance to main faults in 11, curvature and elevation in 10, and drainage network, vegetation and land use potential datasets in 8 studies. In a nother similar study, 117 studies in the literature were examined to determine the parameters used to develop landslide susceptibility maps. According to the results, it was detected that 94.02% of the studies have used curvature, 67.52% lithology, 63.25% aspect, 51.28% drainage characteristics, and 50.43% elevation (AFAD \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this context, the aim of this study is to develop a GIS-supported landslide susceptibility maps in Karaburun Peninsula, İzmir, using FR method. As Thanh et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also underlined, FR is a very popular bivariate statistical method for the assessment of the landslide susceptibility, since it is easy to use and provide good results. Reciever Operating Characteristic (ROC) analysis was performed for the determination of accuracy of the landslide susceptibility map.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Study Area\u003c/h2\u003e\n \u003cp\u003eThe study area is Karaburun Peninsula located between 26\u0026deg; 21\u0026prime;\u0026ndash;26\u0026deg; 38\u0026prime; N longitudes and 38\u0026deg;25\u0026prime;\u0026ndash;38\u0026deg; 40\u0026prime; E latitudes to the west of İzmir province, which is the third biggest city in Turkey (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Karaburun has a coastal length of 130 km (Isik Pekkan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), covers a surface area of 420 km\u003csup\u003e2\u003c/sup\u003e and extends to the Aegean Sea (Prefecture of Karaburun \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e ).\u003c/p\u003e\n \u003cp\u003eThe annual average temperature in the study area varies between 15-20 C\u003csup\u003eo\u003c/sup\u003e and the annual average precipitation is around 650-700 mm. The peninsula is an important area for a good number of wind farm projects, since the wind speed reaches to 50 km/hours in the region (Prefecture of Karaburun \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The terrain of the peninsula is quite rugged and the highest spot in the region is Akdağ mountain with an elevation of 1212 m. Mountains of limestone and andesite surround the sea-level plains and tectonic pits (İzmir Kalkınma Ajansı (İZKA) 2013; Kalafat\u0026ccedil;ıoğlu \u003cspan class=\"CitationRef\"\u003e1961\u003c/span\u003e). This topographic structure results in the formation of a diversity of attractive bays along the coastal areas of Karaburun, which makes the area a popular tourist destination. Road, tourism facility, wind farm construction activities have therefore become very intensive for the last 10 years. The low-density population in the region increases especially during the tourism seasons (Prefecture of Karaburun \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eKaraburun is also a very significant area in terms of its unique landscape characteristics and hosts marine, coastal, mountain, forest and wetland ecosystems (Isik Pekkan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, the region was declared Special Environment Protection Site on 15.03.2019 by the Ministry of Environment and Urbanization. Considering both this new conservation status of the peninsula and the delicate characteristics and increasing demand in the area for tourism and wind farm establishments, it has become an essential requirement to conduct more comprehensive researches in the study area.\u003c/p\u003e\n \u003cp\u003eFrom this perspective, disaster based studies, risk and hazard determination works are also important in the area, so that proper planning and implementation works can be made to protect the existing characteristics and mitigate the potential negative impacts on the ongoing and planned activities. The landslide susceptibility maps are also necessary to determine the landslide hazards in the region\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Material and Data\u003c/h2\u003e\n \u003cp\u003eThe main material used in this study is the spatial data of the Karaburun Peninsula. ArcGIS, Microsoft Excel and IBM SPSS Statistics software were used for data processing, analyses and visualitation processes. The spatial data of the study were transformed into Shape file (*.shp) format.\u003c/p\u003e\n \u003cp\u003eTable 1 summarizes the spatial data and the type of the spatial analysis used as well as the parameter maps developed within the aim of the study\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003eSpatial data of the study\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Taba\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter Map\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName of\u003c/p\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of Data\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of Analysis\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\u003eLandslide Inventory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandslide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandslide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation, slope, aspect, curvature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Elevation Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASTER Global Digital Elevation Model V003 / 30 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopographic analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand use map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorine Land Cover Change /100 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeologial formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClassificaiton\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity analysis and classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to rivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to rivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASTER Global Digital Elevation Model V003 / 30 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrological and proximity anaylsis, classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to fault lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to fault lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity analysis and classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegatation cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8 TM / 30 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDVI index, classsification\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\u003eThe primary data of this study is the existing landslide areas (landslide inventory), which was obtained from the earthsciences portal of General Directorate of Mineral Research and Exploration (MTA) and used for the production of statistical data from particular parameters which initiate the landslides. Since only 3 landslide areas exist in Karaburun Peninsula according to the records, all 33 former landslide areas within İzmir province were used to obtain significant statistical results. Still, the amount of existing landslide areas is considered low compared to the size of the study area, especially when the approaches in other similar studies are exmained (Basharad 2016; Chen 2014; Chen 2016; Akg\u0026uuml;n et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3. Methods\u003c/h2\u003e\n \u003cp\u003eThis study is based on the development of landslide susceptibility maps of Karaburun Peninsula, Izmir province, using method namely FR .Weighted parameter maps were overlaid via GIS capabilities to produce the landslide susceptibility map of the study area. Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the workflow of the study.\u003c/p\u003e\n \u003cp\u003eFor the development of the parameters maps in the study area various methods were used. NDVI technique was applied to determine the vegetation cover (Esendal et al. 2018). Buffer analysis was performed for the determination of the buffer zones around linear fetaures such as roads, rivers and fault lines. Topographic analysis were made to produce elevation, slope and slope maps. Land use parameter map was produced by the reclassification and organization of the CORINE map of the study area. Table 1 also summarizes the main methods utilized for the development of the parameter maps.\u003c/p\u003e\n \u003cp\u003eDetailed information related to parameter map development and FR method utilization processes are provided in the following sections.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.3.1. Development of Parameter Maps\u003c/h2\u003e\n \u003cp\u003eAlthough landslide susceptibility map development studies have become widespread, there is still no consensus on the determination of the necessary parameters and the methods (G\u0026ouml;k\u0026ccedil;eoğlu and Ercanoğlu \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). Therefore, previous studies were examined and referred for the determination of the parameters (Akg\u0026uuml;n et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; G\u0026ouml;k\u0026ccedil;ekoğlu et al. 2001; Huang and Zhao \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kelarestaghi and Ahmadi \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; \u0026Ouml;zdemir and Altural \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pachauri and Pand 1992; Sarkar and Kanungo \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Youssef et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The parameters for this study was determined as elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines.\u003c/p\u003e\n \u003cp\u003eParameter data were obtained from various resources and classified in the first place to statistically evaluate the relation between these data and the characteristics of the existing landslide within the study area to produce the landslide susceptibility map. The resolution was resampled to 30 meters. The existing landslide areas located to the north of the study area around \u0026Ccedil;andarlı Bay region, which cover a considerably large surface, were extracred from the process due to their possible negative influence on the results of the statistical analyses. Therefore, a total of 33 previous landslide areas were used for the study. After the parameter maps were developed, they were reclassified in accordance with the sublayers in the maps based on the information in the literature. Each reclassified parameter map was masked with the polygons comprising the 33 landslide areas in the study area and the number of the pixels in each masked area was calculated to determine the frequency values.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.3.2. Frequency Ratio Method (FR)\u003c/h2\u003e\n \u003cp\u003eFR method provides objective weighting of the parameter maps together with their sublayers in determining the landslide susceptibility areas and is based on the principle of computing the observation frequency of parameter sublayers in landslide areas. FR method enables to associate the sublayers of the parameter maps in the study area with the ones that of existing landslide areas. FR method runs on a probability model that defines the likelihood of an event happening (Ataol and Yeşilyurt \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e, Pham, et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, Soyoung, et al. 2013, Silalahi, et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The formula used for FR method is given below;\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$FR = PLO \\backslash PIF$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PLO\\)\u003c/span\u003e\u003c/span\u003e represents the percentage of the presence of landslides within each sublayer that affect the landslide, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PIF\\)\u003c/span\u003e\u003c/span\u003e is the percentage of each sublayer affecting the landslide in the parameter map.\u003c/p\u003e\n \u003cp\u003eThe FR calculated via this formula is used for assigning the weights for parameter maps (Erener and Lacasse \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Demir \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Landslide susceptibility map is developed by the addition of the weighted parameter maps, in which lower values show the low landslide susceptibility, while high ones correspond to the high landslide susceptibility. These values are reclassified with equal intervals to obtain the final state of the landslide susceptibility map.\u003c/p\u003e\n \u003cp\u003eRegardingly, in this study, the number of pixels corresponding to the landslide area of each sublayer in the relevant parameter map was determined and then the FRs of the sublayers in the landslide areas were calculated using this value.\u003c/p\u003e\n \u003cp\u003eReceiver Operating Characteristic (ROC) analysis was used for the determination of the accuracy of the landslide susceptibility maps produced with FR. ROC analysis is one of the methods used to organize the classifications obtained by statistical processes and to evaluate their performance. Frequently used for medicine bioinformatics, finance and GIS studies, ROC analysis provides the comparison of the statistical models with the real data. When the area below the curve is close to 1, the specificity of the model increases (Mas et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e, Fawcett \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.1. Parameter and Landsclide Susceptibility Maps\u003c/h2\u003e\n \u003cp\u003eIn accordance with the methodology explained in the relevant sections, 10 parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines) were developed in the first place for Karaburun Peninsula. The parameter maps were used for the development of the landslide susceptibility map of the study area. Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e illustrate the parameter maps of the study and Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e shows the final map (landslide susceptibility map) developed with FR method.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrequency table\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency Value\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\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5\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\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6\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\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.1\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\"\u003e\n \u003cp\u003e300-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.5\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\"\u003e\n \u003cp\u003e400-500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003e500-600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.1\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\"\u003e\n \u003cp\u003e600-700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2\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\"\u003e\n \u003cp\u003e700-800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\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\"\u003e\n \u003cp\u003e800-900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\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\"\u003e\n \u003cp\u003e900-1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\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\"\u003e\n \u003cp\u003e1000-1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\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\"\u003e\n \u003cp\u003e1100-1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\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\"\u003e\n \u003cp\u003e1200-1300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\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\"\u003e\n \u003cp\u003e1300-1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.4\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\"\u003e\n \u003cp\u003e1400-1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.1\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\"\u003e\n \u003cp\u003e1500-1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.5\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\"\u003e\n \u003cp\u003e1600-1700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0\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\"\u003e\n \u003cp\u003e1700-1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e1800-1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e2000-2100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e2100-2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e2200-2300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\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\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\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\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003eSoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.1\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\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.1\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\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.6\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\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.9\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\"\u003e\n \u003cp\u003eNortwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\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=\"char\"\u003e\n \u003cp\u003e90.4\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\"\u003e\n \u003cp\u003e10-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003e20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.2\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\"\u003e\n \u003cp\u003e30-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.7\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\"\u003e\n \u003cp\u003e40-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.7\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\"\u003e\n \u003cp\u003e50-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e60-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003e70-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurvature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-29)-(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.7\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\"\u003e\n \u003cp\u003e(-1)-(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003e(-0.5)-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.4\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\"\u003e\n \u003cp\u003e0-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.8\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\"\u003e\n \u003cp\u003e0.5-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.9\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\"\u003e\n \u003cp\u003e1-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.9\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\"\u003e\n \u003cp\u003eTransport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.9\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\"\u003e\n \u003cp\u003eConstruction and Mining Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003eAgricultural Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.3\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\"\u003e\n \u003cp\u003eForests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\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\"\u003e\n \u003cp\u003eGrasslands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003eCoastal Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003eBareen Rocky Lands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\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\"\u003e\n \u003cp\u003eWater Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1\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\"\u003e\n \u003cp\u003eUrban Area, Semi Desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\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\"\u003e\n \u003cp\u003eUrban Area, Dry Soil, Clay Surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\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\"\u003e\n \u003cp\u003eMoist Soil, Bare Soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.9\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\"\u003e\n \u003cp\u003eForest, Grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003eForest, Farmland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.2\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\"\u003e\n \u003cp\u003eDense Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuatermary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2\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\"\u003e\n \u003cp\u003eNeogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\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\"\u003e\n \u003cp\u003ePaleogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003eMesozoic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\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\"\u003e\n \u003cp\u003ePaleozoic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\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\"\u003e\n \u003cp\u003ePrecambrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.9\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\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.0\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\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.1\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\"\u003e\n \u003cp\u003e300-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.2\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\"\u003e\n \u003cp\u003e\u0026lt; 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to rivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.9\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\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.3\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\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.0\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\"\u003e\n \u003cp\u003e300-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.5\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\"\u003e\n \u003cp\u003e\u0026lt; 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to fault lnes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.4\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\"\u003e\n \u003cp\u003e50-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.5\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\"\u003e\n \u003cp\u003e100-150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\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\"\u003e\n \u003cp\u003e150-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.5\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\"\u003e\n \u003cp\u003e200-250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.7\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\"\u003e\n \u003cp\u003e250-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.0\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\"\u003e\n \u003cp\u003e300-350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.3\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\"\u003e\n \u003cp\u003e350-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.4\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\"\u003e\n \u003cp\u003e400-450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.9\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\"\u003e\n \u003cp\u003e450-500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.6\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\"\u003e\n \u003cp\u003e\u0026lt; 500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.4\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\u003eThe table above shows the calculated frequency values. A susceptibility map was produced using these values in the Karaburun Peninsula. When the frequency values were examined, it was seen that the frequencies were evenly distributed or concentrated in a single class in some parameters. This reduces the effect of these parameters on the susceptibility map. Field studies on each of these parameters or other studies to increase accuracy are required.\u003c/p\u003e\n \u003cp\u003eAccording to the landslide susceptibility map (Figure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e) of İzmir province and Karaburun Peninsula, 10% of the study area falls into low risk category and 50% into medium risk category. 40% and 50% of the existing landslide areas comprise medium and high risk zones, respectively. The results show that Karaburun peninsula is mostly made up of high (36%) and medium (52%) risk categories. Still, 10% of the region has low risk characteristics in terms of landslide susceptibility.\u003c/p\u003e\n \u003cp\u003eHigher elevations within the peninsula have medium landslide susceptibility. Karaburun and Mordoğan districts are mainly made up of medium susceptibility areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.2. Accuracy Results\u003c/h2\u003e\n \u003cp\u003eIn a good number of studies ROC method have been used for the determination of the accuracy of the landslide susceptibility maps. In this study, ROC analysis, which is particularly used in biostatistics for the evaluation of performance of the diagnosis and the test was adopted. In ROC, the area below the curve shows the quality of the relation between the test and the diagnoses. The area below the curve line is between 0.5 and 1, where 1 refers to a perfect specificity and 0.5 random specificity (Ayalew and Yamagishi \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reyhanlıoğlu Ke\u0026ccedil;eoğlu et al. 2016).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e illustrates the ROC results of the landslide susceptibility maps of the study area. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the information related to the ROC analyses.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInformation realted to ROC curve\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAsymptotic Sig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAsymptotic 95% Confidence Interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower Bound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper Bound\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\u003eLandslide susceptibility map (FR method)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAccording to ROC analysis, the accuracy of the landslide susceptibility map developed with FR method was found 78.3%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this study, FR method was utilized for the production of the landslide susceptibility map, and thus to spatially predict the landslide risks in Karaburun Peninsula, İzmir province. The study area is a delicate region in terms of landscape characteristics and a popular tourist destination. Landslide susceptibility map development efforts are based on the analysis of natural, environmental and triggering parameters by using different statistical methods. In the study, most common 10 parameters (elevation, aspect, slope, curvature, lithology, land use, vegetation cover, distance to the roads, distance to fault lines and distance to the rivers), which were frequently used in previous studies, were analyzed using FR method. The data and the methods of the study were determined in accordance with the literature review. All the parameter maps were organized to have the same standards in terms of study area boundaries, type, scale, raster resolution and coordinate system. In addition, experts were consulted to determine the relation between the landslides and the triggering parameters. Regarding the process and the produced landslide susceptibility map, the followings were concluded.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIn FR method, the influence of \u0026ldquo;distance to rivers\u0026rdquo; parameter was found considerably low. This is thought to be mostly because the winter flows are very common in the region. The streams in the region are generally dry for most of the year. Increase in the pore water pressure is one of the reasons that cause landslides. This is assumed to be the reason for the landslides in the study area to occur mainly in the coastal areas.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAccording to the MTA database, only 3 former landslides were recorded in Karaburun Peninsula. Due to this low number, all the existing landslide data for whole İzmir province were used to obtain statistically significant results. On the other hand, AFAD also keeps the inventory of the landslides in the country. From this point, it is necessary to use the data of both institutions to obtain more accurate results. Field surveys, satelleite imagery, UAV and LIDAR applications are also utilized for the determination of the landslides. Thefore, it is essential to update, integrate and enrich the exsting landslide inventory with the support of the advanced technologies. Thus, it will be possible for researches to develop more accurate and precise maps.\u003c/li\u003e\n \u003cli\u003eA good number of wind farm establishments exists within the study area, mostly erected on the higher elevations. Accordingly, many roads have been built to provide transportation to the wind farms. However, the road data used in this study excludes these mentioned roads, which in fact may be important triggering factors and should be obtained and analysed for further works. The authors believe that these ignored roads are likely to increase the landslide susceptibility in the region.\u003c/li\u003e\n \u003cli\u003eThe landslide susceptibility map revealed that the Karaburun Peninsula mostly comprised medium risk lands. In this study yhe number of landslide inventory is rather low compared to similar studies. Srill, it is of significance to allow for the landslide risks for strategic and physical planning processes in the region as well as a range of economic, social and environmental works.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent to Publish\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization, data collection, analysis, and first draft writing stages were performed by Muhittin Ozan KARAMAN. Saye Nihan \u0026Ccedil;ABUK and Emrah PEKKAN supervised and contributed to the context and medthod development, evaluation of the results, and final writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAFAD (2008) T\u0026uuml;rkiye Heyelan yoğunluk Haritası. https://www.afad.gov.tr/kurumlar/afad.gov.tr/3506/xfiles/96-2014060215311-heyelan_yogunluk_a1_olceksiz.pdf. \u0026nbsp;Accessed 10 June 2019\u003c/li\u003e\n \u003cli\u003eAFAD (2015). B\u0026uuml;t\u0026uuml;nleşik Tehlike Haritalarının Hazırlanması Heyelan ve Kaya D\u0026uuml;şmesi Pratik Kılavuz. AFAD, Ankara\u003c/li\u003e\n \u003cli\u003eAFAD (2020) Afet Y\u0026ouml;netimi Kapsamında 2019 Yılına Bakış ve Doğa Kaynaklı Olay İstatistikleri. https://www.afad.gov.tr/kurumlar/afad.gov.tr/e_Kutuphane/Kurumsal-Raporlar/Afet_Istatistikleri_2020_web.pdf. \u0026nbsp;Accessed 10 June 2019\u003c/li\u003e\n \u003cli\u003eAkg\u0026uuml;n A, Dağ S, Bulut \u0026nbsp;F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54 (6):1127-1143. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/s00254-007-0882-8\" target=\"_blank\"\u003e10.1007/s00254-007-0882-8\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eAlcantara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphol 47(2-4):107-124. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1016/S0169-555X(02)00083-1\" target=\"_blank\"\u003e10.1016/S0169-555X(02)00083-1\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eAtaol M, Yeşilyurt S (2014) \u0026Ccedil;ankırı-Ankara Karayolu Boyunca (Akyurt-\u0026Ccedil;ankırı Arası) Heyelan Risk B\u0026ouml;lgelerinin Belirlenmesi . Cograf Derg 29:51-69\u003c/li\u003e\n \u003cli\u003eAyalew L, Yamagishi H (2005) The application og GIS-based logistic regression for landslide suspectibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphol 65(1-2):15-31. \u003ca href=\"https://doi.org/10.1016/j.geomorph.2004.06.010\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.geomorph.2004.06.010\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eBasharad M, Shah H, Hameed N (2016) Lanslide Suspectibility Mapping Using GIS and Weighted Overlay Method: A Case Study from NW Himayalas, Pakistan. Arab J Geosci\u0026nbsp;9:292(2016).\u0026nbsp;https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/s12517-016-2308-y\" target=\"_blank\"\u003e10.1007/s12517-016-2308-y\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eBui DT, Tsangaratos P, Nguyen VT, Van Liem N, \u0026nbsp; \u0026nbsp;Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning \u0026nbsp; models in landslide susceptibility assessment. Catena 188:104426. \u003ca href=\"https://doi.org/10.1016/j.catena.2019.104426\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.catena.2019.104426\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eChen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016) A GIS-Based Comparative Study of Frequency Ratio, Statistical Index and Weights \u0026ndash;of-Evidence Models in Lanslide Suspectibility Mapping. Arab J Geosci\u0026nbsp;9:204. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007%2Fs12517-015-2150-7\"\u003e10.1007/s12517-015-2150-7\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eChen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014). Lanslide Suspectibility Mapping Based on GIS and Information Value Model for the Chencang District og Bajoi, China. Arab J Geosci 7:4499-4511. https://doi.org/10.10007/s12517-014-1369-z\u003c/li\u003e\n \u003cli\u003eChen, X., \u0026amp; Chen, W. (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena 196:104833. \u003ca href=\"https://doi.org/10.1016/j.catena.2020.104833\"\u003ehttps://doi.org/10.1016/j.catena.2020.104833\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eChen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195:104777.\u0026nbsp;https://doi.org/\u003ca href=\"http://dx.doi.org/10.1016/j.catena.2020.104777\" target=\"_blank\"\u003e10.1016/j.catena.2020.104777\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u0026Ccedil;an T, Duman T, Olgun Ş, \u0026Ccedil;\u0026ouml;rek\u0026ccedil;ioğlu \u0026nbsp; \u0026nbsp;Ş, \u0026nbsp;G\u0026uuml;lmez F, Elmacı H, Hamza\u0026ccedil;ebi S, \u0026nbsp;Emre \u0026Ouml; (2013). T\u0026uuml;rkiye Heyelan Veri Tabanı. https://www.hkmo.org.tr/resimler/ekler/85a47f65233d5d0_ek.pdf . Accessed 20 June 2021\u003c/li\u003e\n \u003cli\u003eDemir G (2018) Coğrafi Bilgi Sistemleri ile Suşehri (Sivas) Heyelan Duyarlılık Analizi. GUSTIJ 8:96-112. \u003ca href=\"https://doi.org/10.17714/gumusfenbil.299987\"\u003ehttps://doi.org/10.17714/gumusfenbil.299987\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eErcanoğlu M, Hasekioğulları G, G\u0026uuml;nal B (2008) Heyelan Duyarlılığı \u0026Ccedil;alışmalarında T\u0026uuml;rkiye\u0026apos;nin Uluslararası Bilimsel Literat\u0026uuml;rdeki Yeri. Bull Eng Geol \u0026nbsp;26-27:35-51\u003c/li\u003e\n \u003cli\u003eErener A, Lacasse S (2007) Landslide susceptibility mapping using GIS. In TMMOB Chamber of Survey and Cadastre Engineers National Geographic Information Systems Congress. Trabzon,Turkey.\u003c/li\u003e\n \u003cli\u003eEsendal Bozkurt N, Zontul M, Aslan Z (2018) Uydu Verilerine Dayalı Olarak Bitki \u0026Ouml;rt\u0026uuml;s\u0026uuml; Analizi. Aurum J Eng Syst Archit 2(1):75-82\u003c/li\u003e\n \u003cli\u003eFang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321-347. \u003ca href=\"https://doi.org/10.1080/13658816.2020.1808897\"\u003ehttps://doi.org/10.1080/13658816.2020.1808897\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eFawcett T, (2006) An Introduction to ROC Analysis. Pattern Recognit Lett 27-8:861-874\u003c/li\u003e\n \u003cli\u003eGariano SL, \u0026nbsp;Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227252\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;k\u0026ccedil;eoğlu C, Ercanoğlu M (2001) Heyelan Duyarlılık Haritalarının Hazırlanmasında Kullanılan Parametreleri İlişkin Belirsizlikler. Bull Earth Sci 23:189-206.\u003c/li\u003e\n \u003cli\u003eHighland LM, Bobrowsky P (2008) The landslide handbook\u0026mdash;A guide to understanding landslides https://pubs.usgs.gov/circ/1325/pdf/Sections/Section1.pdf . Accessed 20 June 2021\u003c/li\u003e\n \u003cli\u003eHuang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. Catena 165:520\u0026ndash;529. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1016/j.catena.2018.03.003\" target=\"_blank\"\u003e10.1016/j.catena.2018.03.003\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eİZKA (2013) Urla-\u0026Ccedil;eşme-Karaburun Yarımadası S\u0026uuml;rd\u0026uuml;r\u0026uuml;lebilir Kalkınma Stratejisi http://izka.org.tr/wp-content/uploads/pdf/14_yarimada_kalkinma_stratejisi.pdf . \u0026nbsp;Accessed 20 June 2021\u003c/li\u003e\n \u003cli\u003eIsik Pekkan \u0026nbsp;O, Kurkcuoglu \u0026nbsp;MAS, Cabuk SN, Aksoy T, Yilmazel B, Kucukpehlivan T, Dabanli A, Cetin M (2021) Assessing the effects of wind farms on soil organic carbon. Environ Sci Pollut Res 28:18216-18233. \u0026nbsp;\u003ca href=\"https://doi.org/10.1007/s11356-020-11777-x\" target=\"_blank\"\u003ehttps://doi.org/10.1007/s11356-020-11777-x\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eKalafat\u0026ccedil;ıoğlu A (1961) A Geological Study in the Karaburun Peninsula . Bull Miner Res Explor 56 (56):40-49\u003c/li\u003e\n \u003cli\u003eKe\u0026ccedil;ecioğlu \u0026Ccedil;, Gelbal S, Doğan N (2016) ROC Eğrisi ile Kesme Puanının Belirlenmesi. J Soc Sci\u0026nbsp;50:553-562.\u0026nbsp;https://doi.org/\u003ca href=\"http://dx.doi.org/10.9761/JASSS3564\" target=\"_blank\"\u003e10.9761/JASSS3564\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eKelarestaghi A, Ahmadi H (2009) \u0026nbsp;Landslide suspectibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95-101. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/s12517-008-0022-0\" target=\"_blank\"\u003e10.1007/s12517-008-0022-0\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eKirschbaum D, Kapnick SB, Stanley T, Pascale S (2020) Changes in extreme precipitation and landslides over High Mountain Asia. Geophys Res Lett 47: e2019GL085347. \u0026nbsp;\u003ca href=\"https://doi.org/10.1029/2019GL085347\"\u003ehttps://doi.org/10.1029/2019GL085347\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLacroix P, Dehecq A, Taipe E (2020) Irrigation-triggered landslides in a Peruvian desert caused by modern intensive farming. Nat Geosci 13(1):56-60. \u003ca href=\"https://doi.org/10.1038/s41561-019-0500-x\"\u003ehttps://doi.org/10.1038/s41561-019-0500-x\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33-41.\u0026nbsp;https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/s10346-006-0047-y\" target=\"_blank\"\u003e10.1007/s10346-006-0047-y\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLiu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, \u0026nbsp;Lacasse S (2021) Modelling \u0026nbsp; \u0026nbsp; \u0026nbsp; of shallow landslides with machine learning algorithms. Geosci Front 12(1):385-393. \u003cem\u003ehttps://doi.org/\u003c/em\u003e\u003ca href=\"http://doi.org/10.1016/j.gsf.2020.04.014\" target=\"_blank\"\u003e10.1016/j.gsf.2020.04.014\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMas J, Filho B, Pontius R, Gutierrez M, Rodriques H (2013) A Suite of Tools for ROC Analysis of Spatial Models. ISPRS Int. J. Geo-Inf 2:869-887. \u003ca href=\"https://doi.org/10.3390/ijgi2030869\"\u003ehttps://doi.org/10.3390/ijgi2030869\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7:20. \u003ca href=\"https://doi.org/10.1186/s40677-020-00155-x\"\u003ehttps://doi.org/10.1186/s40677-020-00155-x\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMerghadi A, Yunus AP, Dou Jie, Whiteley Jim, ThaiPham B, Bui DT, Avtar R, Abderrahmane \u0026nbsp;B (2020) Machine learning methods for landslide suspectibility studies: A comparative overview of algorithm performance. Earth-Sci Rev 207:103225. \u003ca href=\"https://doi.org/10.1016/j.earscirev.2020.103225\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.earscirev.2020.103225\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eNsengiyumva JB, Valentino R (2020) Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment. Geomat \u0026nbsp;Nat \u0026nbsp; \u0026nbsp;Hazards Risk, 11(1):1250-1277. \u003ca href=\"https://doi.org/10.1080/19475705.2020.1785555\"\u003ehttps://doi.org/10.1080/19475705.2020.1785555\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;zdemir A, Altural T (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180-197. \u003ca href=\"https://doi.org/10.1016/j.jseaes.2012.12.014\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.jseaes.2012.12.014\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003ePachauri A, Pant M (1992). Landslide Hazard Mapping Based on Geological Attributes. Eng Geol \u0026nbsp;32 (1-2):81-100. \u003ca href=\"https://doi.org/10.1016/0013-7952(92)90020-Y\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/0013-7952(92)90020-Y\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003ePham BT, Dieu TB, Prakash I, Dholakia MB (2015). Landslide Suspectibility Assessment at a Part of Uttarakhand Himalaya, India using GIS-based Statistical Approach of Frequency Method. Int J Eng Res Technol 4(11):338-344. http://dx.doi.org/10.17577/IJERTV4IS110285\u003c/li\u003e\n \u003cli\u003ePrefecture of Karaburun (2019) http://www.karaburun.gov.tr. Accessed 10 July 2019\u003c/li\u003e\n \u003cli\u003eSoyoung P, Chuluong C, Byungwoo K, Jinsoo K (2012) Landslide Susceptibility Mapping Using Frequency Ratio, Analytic Hierarchy Process, Logistic Regression and Artificial Neural Network Methods at the Inje Area, Korea. Env Earth Sci 68(5):1443-1464.\u0026nbsp;https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/s12665-012-1842-5\" target=\"_blank\"\u003e10.1007/s12665-012-1842-5\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003ePasang S, Kub\u0026iacute;ček P (2020) Landslide susceptibility mapping using statistical methods along the Asian Highway, Bhutan. Geosci 10(11):430. \u0026nbsp;\u003ca href=\"https://doi.org/10.3390/geosciences10110430\"\u003ehttps://doi.org/10.3390/geosciences10110430\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003ePrakash N, Manconi A, Loew S (2020) Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. \u003cem\u003eRemote Sens 12(3)\u003c/em\u003e:346. \u003ca href=\"https://doi.org/10.3390/rs12030346\"\u003ehttps://doi.org/10.3390/rs12030346\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eSahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci 2(7):1-17. \u003ca href=\"https://doi.org/10.1007/s42452-020-3060-1\"\u003ehttps://doi.org/10.1007/s42452-020-3060-1\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eSarkar S, Kanungo DP (2017) GIS Application in Landslide Susceptibility Mapping of Indian Himalayas. In: Yamagishi H, Bhandary NP (ed) GIS Landslide. Springer, Tokyo, pp 211\u0026ndash;219. https://doi.org/\u003ca href=\"http://dx.doi.org/10.1007/978-4-431-54391-6_12\" target=\"_blank\"\u003e10.1007/978-4-431-54391-6_12\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eSilalahi FES, Pamela Arifianti Y, Hidayat F (2019) \u0026nbsp;Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci. Lett. 6:10. \u003ca href=\"https://doi.org/10.1186/s40562-019-0140-4\"\u003ehttps://doi.org/10.1186/s40562-019-0140-4\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eThanh DQ, Nguyen DH, Prakash I, Jaafari A, Nguyen VT, Van Phong T, Pham BT (2020). GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong, Vietnam Vietnam J Earth Sci 42(1):55-56. https://doi.org/10.15625/0866-7187/42/1/14758\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;UNISDR (2018) Economic Losses, Poverty and Disasters: 1998-2017. \u003ca href=\"https://www.preventionweb.net/files/61119_credeconomiclosses.pdf\"\u003ehttps://www.preventionweb.net/files/61119_credeconomiclosses.pdf\u003c/a\u003e. Accessed 28 August 2021\u003c/li\u003e\n \u003cli\u003eWang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers Geosci 138:104445. https://doi.org/\u003ca href=\"https://ui.adsabs.harvard.edu/link_gateway/2020CG....13804445W/doi:10.1016/j.cageo.2020.104445\" target=\"_blank\"\u003e10.1016/j.cageo.2020.104445\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eZhang YX, Lan HX, Li LP, Wu YM, Chen JH, Tian NM (2020) Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China. J Mt Sci 17(2):340-357. \u003ca href=\"https://doi.org/10.1007/s11629-019-5702-6\"\u003ehttps://doi.org/10.1007/s11629-019-5702-6\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eYoussef AM, Al-Kathery M, Pradhan B (2015). Landslide suspectibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci \u0026nbsp; \u0026nbsp; \u0026nbsp; J 19:113-134. \u003ca href=\"https://doi.org/10.1007/s12303-014-0032-8\"\u003ehttps://doi.org/10.1007/s12303-014-0032-8\u003c/a\u003e\u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Karaburun Peninsula, GIS, Landslide, Landslide suspectibility mapping, Frequency ratio, Parameter maps","lastPublishedDoi":"10.21203/rs.3.rs-1127725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1127725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeographical information systems (GIS) facilitates both current landslide mapping processes and prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were developed in order to reduce the damages of possible landslides in Karaburun Peninsula of İzmir province. To fulfil this aim, landslide inventory map was produced from related databases in the first place followed by the development of parameter (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers and distance to fault lines) maps. Frequency ratio method was utilized for developing the landslide susceptibility maps and Reciever Receiver Operating Characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 36% and 52% of the study area had high and medium risk categories, respectively. 10% of the region has low landslide risk. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been formerly declared as a special protection area, and is a popular destination for both tourism activities and energy facilities.\u003c/p\u003e","manuscriptTitle":"Utilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-12 16:47:39","doi":"10.21203/rs.3.rs-1127725/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2022-04-21T12:28:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-12T10:35:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-10T14:52:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-12-14T05:16:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-11-30T07:33:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"238f3aa3-56e8-419e-85b8-b4959d468ca7","owner":[],"postedDate":"January 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-07-05T12:32:10+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-12 16:47:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1127725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1127725","identity":"rs-1127725","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}