Enhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India

Enhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India | 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 Enhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India Ankita Sharma, Radha Krishan, Bhaskar Nikam, Dhirendra Singh Bagri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598195/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban growth monitoring and assessment are crucial for sustainable long-term planning and the efficient utilization of natural resources. Unplanned urbanization poses risks such as pollution and environmental disruption, emphasizing the need for proactive management. Changes in Land Use and Land Cover (LULC) with time indicate ongoing urbanization trends. This study focuses on predicting and simulating urban growth in the Asan watershed, Uttarakhand, India until 2040 using the SLEUTH model and assessing the impact on the surrounding areas. The research utilizes primary datasets from 1980 to 2016, including the Digital Elevation Model (DEM) and LULC, to forecast future urban expansion. The SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, based on Cellular Automata (CA) principles, is employed to simulate urban growth by analysing and projecting LULC changes from 2016 to 2040. Recent methodologies prioritize the detection of LULC changes through multispectral satellite images, emphasizing factors like radiometric efficiency, spatial uniformity, and climatic conditions. The predicted urban growth output revealed that the projected increase of urban area by 2040 will be 67.73 km² from 36.5 km² in 2016 with an increment of 31.23 km². Additionally, by 2040, urban settlement is expected to occupy around 9.5% of the total watershed area, an increase of 4.3% from the urban area observed in 2016. The study aims to guide infrastructure planning and promote sustainable development practices by comprehending urban dynamics, growth patterns, and resource management. Urban Studies Urban growth assessment Urban dynamics Growth patterns calibration & prediction LULC SLEUTH model Sustainable development 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 1. INTRODUCTION Monitoring and assessing urban growth is critical for long-term planning and optimal utilisation of natural resources, also this helps in reducing the risk of issues arising from unplanned urbanisation, such as pollution and environmental disruption. The rapid pace of urbanization and industrialization has led to significant alterations in land use/land cover (LULC) over the last two centuries, which indicates that this pace of alteration will continue to boost in the coming years. Burdett (2012) defines urban growth as ‘the expansion of an area with the number and size of its inhabitants’. The desire for facilities, as well as the need for employment, are the primary drivers of urban expansion (Garg, et al. 2012 ). However, unplanned growth causes a slew of problems such as changing climate, over-exploitation of natural resources, and insufficient infrastructure. Unplanned land use, poor resource management, poor public policy and funding level are all factors contributing to a lack of understanding of urban dynamics, particularly in developing countries (Xian et al. 2005). Several LULC change detection methodologies have been introduced recently for monitoring topographical change and urban growth. The radiometric efficiency of the data, the level of uniformity at a particular location and climatic conditions are the modern approaches used to extract LULC maps from multispectral satellite images (Garg, et al. 2012 ). Urban growth modelling is a critical tool in analysing and predicting the dynamics of urban expansion. Silva & Clarke ( 2002 ) highlight the value of these models in providing policymakers and planners with the ability to anticipate and forecast future changes or trends in urban development. This capability allows them to understand the impacts of future development and explore the potential consequences of different policies (Pettit et al., 2002 ; Verburg et al., 2002 ). The dynamics of urban growth are inherently complex and nonlinear. Urban growth refers to the expansion of towns and cities, typically measured by the increase in the size of built-up areas. This expansion is driven by various factors, including the requirements of the city, available facilities, and the level of industrialization. These factors contribute to the migration of people from rural to urban areas, exerting significant pressure on infrastructure and natural resources. The migration can lead to the formation of slums, increased traffic congestion, the reduction of farmlands, deforestation and threats to wildlife. Understanding these dynamics through modelling can help manage the negative impacts of urban growth. Effective urban growth models enable planners to make informed decisions that balance development needs with environmental and social considerations, promoting sustainable urbanisation. Various models have been developed to analyse the LULC change over time and forecast the development of future classes of landuse. These models are based on different modelling approaches and can be categorized as follows: Empirical Statistical Model, Stochastic Model, Optimization Models, Dynamic Process-Based Simulation Model, Cellular Automata Model and Integrated Model. Cellular Automata (CA) models are particularly popular in LULC change analysis for complex regions. These models are based on cellular grids where each grid cell represents a single state at a particular time point. The state change or transition depends on predefined rules. CA-based models can produce spatio-temporal effects with visual interpretations, indicating where the change is taking place. This approach is more popular in LULC change analysis for complex regions and can also be used for long-term prediction. One of the CA-based models widely used for urban growth simulation is the SLEUTH model. This model has been applied in developed countries to simulate urban growth and create urban growth models (Silva & Clarke, 2002 ; Dietzel & Clarke 2006 ; Onsted & Clarke, 2012 ). However, limited studies have been conducted in developing countries for urban growth simulation (Gilbert & Shi, 2023 ). In India, researchers have made efforts to simulate urban growth models for cities such as Pune, Hyderabad, Pushkar, and Ajmer (Gandhi & Suresh, 2012 ; KantaKumar et al. 2011 ; Jat et al. 2017 ; Saxena et al. 2019). In the current study, the focus of the study is on predicting and simulating urban growth in the Asan watershed, Uttarakhand, India, until 2040, and assessing its impact on the surrounding areas using the CA-based SLEUTH model. The research utilizes a primary dataset spanning the last 36 years (1980–2016), including DEM and LULC. This multi-decade analysis reveals the gradual expansion of urban areas within the study area. Building on this historical context, the study aims to forecast future urban expansion, providing valuable insights for infrastructure development modelling and planning. Different physical processes such as deforestation, siltation, and mass wasting significantly impact the hydrology of the study area at different scales (Joshi, 2018 ). Dehradun, a city predominantly engaged in administrative, educational, commercial, defence and tourism sectors, is now rapidly transforming into an industrial and major service centre for the entire region. The watershed is primarily sustained by monsoonal rainfall and spring drains across the Siwalik range with fragile rocks. Degradation of natural resources, as well as the soaring population, is the major concern of the watershed. Over time, changes in LULC have profoundly altered the hydrological dynamics within the watershed. Forests and agricultural lands from the Siwalik and Lesser Himalayas dominate land use in the study area. Following the establishment of Dehradun as the capital city, the region has experienced rapid population growth, which has markedly affected the green cover. The conversion of rural, forest or wetland areas into urban infrastructure such as streets, highways, institutional and commercial systems has changed the conditions of hydrologic runoff. Human activities have significantly influenced surface hydrology by increasing impervious surfaces and altering land use patterns (Yifru, 2022). By considering all the literature reviews and research-based facts, we consider these objectives for our study: (i) Prediction of future urban growth patterns with the help of associated model in terms of geo-environmental aspects and impact of future land use on resource availability (ii) Use historical datasets to track changes in land cover and evaluate these changes through modelling technique. 2. STUDY AREA The Asan River watershed is located in Dehradun district, Uttarakhand, India, encompassing latitudes 30 ° 14' 14'' N to 30 ° 29' 54'' N and longitudes 77 ° 39' 42'' E to 78 ° 05' 30'' E. The area spans elevations from 345m to 2188m above mean sea level (MSL) and covers an area of approximately 712.34 km 2 (Fig. 1 ). The watershed extends for about 40 km in length, with an approximate widest width of 18 km. Situated in Doon Valley at the foothills of the Siwalik ranges, the Asan River flows centrally from the northeast to the southwest. As the river moves toward the southwest, it carries water from several streams flowing southwards from the Lesser Himalayan range and northwards from the Siwaliks. Asan River is the tributary of Yamuna River having an approximate length of 43 km within the watershed’s boundary. In the lower reaches, a relatively flat section flows that feeds the Asan barrage. It is located in the vicinity of the Asan and Yamuna Rivers. The water from the barrage later drains into the Yamuna at Paonta Sahib. 3. DESCRIPTION OF SLEUTH MODEL The SLEUTH model, developed by Clarke & Gaydos ( 1998 ) and later refined by Silva & Clarke ( 2002 ), uses several key layers to simulate urban growth: Slope, Land Use/Land Cover, Exclusion, Urbanization, Transportation (Road), and Hillshade. These components form the acronym SLEUTH and are essential for generating accurate urban growth predictions. This Cellular Automata (CA)-based Models consist of two modules: the Clarke Urban Growth Model (UGM) which is used to simulate urban growth and the Land Cover Deltatron Model (LCD), used to simulate land-use change and land transition. The SLEUTH model operates on a nested loop and is written in the ‘C’ language. The inner loop of the model performs growth rules for a single year and the outer loop implements every growth history repeatedly and stores information about all the cumulative statistical data (Fig. 2 ). The input layer of the urban growth is extracted from the decadal LULC maps of the study area. The growth rule which includes the coefficient values, spreading growth type, number of iterations, topographic slope and proximity to the road network, determines the potential urbanization of cells. These rules result in the expansion of the urban layer in the following year, forming a modified array that reflects the new urban extent (Merryweather-Clarke et al. 1997 ; Clarke & Gaydos, 1998 ). This comprehensive model allows for detailed simulation and analysis of urban growth patterns, providing valuable insights for urban planning and development. In the model, five factors that control the behaviour of the system are: Breed factor coefficient : It determines the probability of new spreading centres and the growth of settlements influenced by roads. Diffusion factor coefficient : It controls the overall spontaneous growth influenced by roads and the search distance along the road network. Spread Coefficient : It controls the diffusion expansion/organic growth that occurs from established urban pixels. Slope resistance : It influences the slope of urban pixels; as the degree of slope increases, the likelihood of settlement decreases. Road gravity : It controls the outward distance at which new urban pixels develop due to road-influenced growth. 4. METHODOLOGY The CA-based urban growth SLEUTH model is evaluated with input layers of three different resolutions coarse, medium, and fine in three phases of calibration namely: coarse, fine and final, respectively. Input data preparation, model calibration, model prediction, and model output are the four major steps in the SLEUTH model execution. Three scenario files are edited by changing the input layers of the adopted resolution, and then independently calibrated and predicted. We used the SLEUTH 3.0 beta p01_module ( https://www.ncgia.ucsb.edu/projects/gig/ ) and Cygwin software to complete the model compilation and operation. The detailed methodology in the form of a flow chart is shown in Fig. 3 . 5. INPUT DATA PARAMETERIZATION The execution of the SLEUTH model requires six types of input maps as mentioned in the acronym SLEUTH and these are Slope, Land Use/Land Cover, Exclusion, Urban, Transportation and Hillshade of different years. The initiation requires slope maps, two decadal LULC maps, one exclusion layer (which includes any LULC class other than urban), urban (built-up) maps for four years, a transportation map for two years, one hillshade map for observing the topography and two decadal LULC maps (Fig. 4 ). The LULC maps for 2008 and 2016 were created using our previous research, which classified eight categories: built-up areas (urban and rural), cropland, fallow land, plantation, evergreen needle leaf forest, deciduous broadleaf forest, mixed forest, and water bodies. These maps were manually digitized through visual interpretation for two different years using Landsat 5 (TM) and LISS-IV imagery (Sharma & Bagri, 2020, 2023; Sharma et al. 2021). In ArcGIS, the road layers initially digitized are transformed into a raster format. To suit the requirements of the model, these road layers need to be represented as binary images. Consequently, pixels corresponding to non-road areas are coded as zero, while those representing roads are coded as one. Similarly, areas that are excluded from the analysis are coded as one, while non-excluded areas are coded as zero (Herold et al. 2003 ; Dietzel & Clarke, 2007 ; KantaKumar et al. 2011 ). The urban (built-up) layer is derived from the extraction of features from these maps (Fig. 5 ). The slope map (in degrees) and hillshade for topography are prepared from Cartosat-1 (10m) using ArcGIS. By digitising roads from reference maps (satellite images and SOI toposheets), two transportation (road) maps have been created (Fig. 6 ). To make an exclusion map, recent waterbodies are extracted from the LULC (2016) (Fig. 7 ). In addition, all inputs were resampled into three datasets for three stages of model calibration with different resolutions: coarse (80m), fine (40m), and final (20m), with an 8-bit radiometric resolution. As a result, for each of the three calibration phases, three different row and column sizes are created. All layers (input raster data) must be in a standardised format, which is an 8-bit unsigned GIF. 6. MODEL EXECUTION A brief account of three phases of SLEUTH model processing namely: 6.1. TEST PHASE: The test phase is used to ensure that the preliminary conditions and input variables are appropriate to meet the standard criteria. It is an initial phase and is critically performed before calibrating the model. After several attempts of a run to validate the criteria, the successful run of the test phase indicates that the desired preliminary conditions required for the model execution have been met, and it is now prepared for the calibration phase. 6.2. CALIBRATION PHASE: The calibration is the most important aspect of the urban growth simulation (Dietzel & Clarke, 2006 ; Jat et al. 2017 ). The primary goal of this phase is to determine the best growth coefficient combination. In this study, the “Brute force parameter estimation” algorithm is used to calibrate the model. Calibration is done in three stages here: coarse, fine, and final. At first, the coarse phase of the model calibration was run which required an input dataset of 80m resolution, the adopted initial values of growth coefficients, and the number of Monte Carlo iterations. Furthermore, the values of the refined growth coefficient were calculated and adopted for other scenario files belonging to the fine (40m) and final (20m) calibration phases based on the computed statistical metrics ( control_stats ) generated as an output of coarse calibration. The higher the number of Monte Carlo iterations chosen, the finer the spatial resolution of the input dataset. 6.3. PREDICTION PHASE: The prediction phase involves running the model with the datasets of the finer resolution, a number of Monte Carlo iterations ≥ 100 and the best-fit growth coefficient generated during the calibration phase. It's a one-step process that generates prediction images as well as statistical data files storing information about urban growth. The entire setup of the model is prepared to predict future urban growth until 2040. 7. RESULTS AND DISCUSSIONS This section presents the result of the urban growth SLEUTH model. The selection and usage of input data parameters for different phases were detailed in the previous parameterization section. The model implementation was divided into three phases: test, calibration and prediction. 7.1. SLEUTH MODEL CALIBRATION: In the calibration phase, there are two methods for estimating model parameters: brute force calibration and genetic algorithm. To find the best coefficient values, the brute force algorithm goes through three stages. On the contrary, the genetic algorithm encompasses an adaptive search of the coefficients space. The calibration phase entails refining growth coefficient values sequentially from one phase to the next (Goldstein, 2004 ). The main objective of the model calibration is to accurately recreate historical urban expansion as it appears in the input datasets. To find the optimum growth coefficient values, a quantitative value is arranged from all the possible iterations that are the best fit. This phase is a highly complex process that must be computed (Jantz et al. 2010 ). Simulation of growth parameters was done several times by setting Monte Carlo iteration and further comparing the simulated and actual growth by determining various information about urban pixels such as their number, size, edge pixel and many others. In the end, the model generates a least square regression metric called ‘control_stats.log’ , for each calibration phase (i.e., coarse, fine and final) which stores information about thirteen parameters such as population, mean cluster size, composite score, edges, compare value, average slope, Lee-Salle metric, urban clusters, X-mean, Y-mean and so on. The performance of these parametric combinations is evaluated using these logs (Abd-Allah, 2007 ). Each metric represents the degree of fit between observed and predicted (modelled) growth. The values of the coefficient range for each calibration phase are determined using a variety of methods. Here in this study Optimum SLEUTH Metric (OSM) method developed by Dietzel & Clarke ( 2007 ), is used to calculate the values of coefficient space using the formula (Eq. 1): OSM = compare * pop * edges * clusters * slope * xmean * ymean 1 This formulation assisted in sorting metrics in descending order by allocating weightage to them and then with the top three highest weightage being used to determine the value of coefficient space (Table 1 ) (Dietzel & Clarke, 2007 ; KantaKumar et al. 2011 ). These values are averaged to get derived growth coefficients from each calibration phase, which are further used as start and stop values for subsequent phase runs (Table 2 ). Growth coefficients from final calibration are also being used in the prediction phase of urban growth. Figure 8 , shows the parametric arrangement of generated file ‘control_stats.log’ for a coarse phase of calibration. The Lee-Sallee metric is a “measure of spatial fit between the modelled urban growth to the known urban extent” (Dietzel & Clarke, 2007 ). As a result, the Lee-Sallee metric is used as a primary criterion to narrow the coefficient space. The parameters of the linear square metric are averaged and considered for three scenarios used in the calibration phases (Table 1 & Table 2 ). Table 1 Parameters assessing the model calibration for the three scenarios Least Square Regression Estimates Parameters Description Scenario 1 Scenario 2 Scenario 3 Compare Modelled final population 0.95 0.93 0.94 Population Number of urban pixels 1.00 0.98 0.98 Edges Urban perimeter 0.99 0.95 0.99 Cluster Urban cluster edge pixels 1.00 0.83 0.89 Lee-Sallee Shape index 0.97 0.96 0.97 Slope Slope of known urban cell 0.99 0.96 0.96 X-mean Average of X values 0.96 0.99 0.99 Y-mean Average of Y values 0.95 0.88 0.83 Table 1 depicts the evaluation of the calibration phase for three scenarios. The 'Compare' metric values of Scenarios 1 and 3 are 0.95 and 0.94 respectively which is higher than Scenario 2 (0.93). The 'Population' metric values are all above 0.97. The 'Edges' represent a significant relation between the urban edge of actual and simulated years depicting the values 0.99, 0.95 and 0.99 for Scenarios 1, 2 and 3 respectively. Along with it, the 'Cluster' metric values are above 0.83 for every scenario demonstrating the model's ability to accurately simulate the urban shape and urban cluster changes. The 'Lee-Sallee' metric represents 0.97 for scenarios 1 and 3 while 0.96 for scenario 2. The ‘ X’ & ‘Y -mean’ metrics are all above 0.83, indicating a significant relationship between urbanised cells of actual and simulated years. The outcome of metric evaluation shows acceptable simulation effects in terms of the area, shape, cluster, and location of the urban cell and enables us to be assured in using urban prediction by 2040. Table 2: Coefficient settings for three scenarios of the different calibration phase Growth Coefficients Growth Coefficient Coarse Fine Final Number of iteration=4 Number of iteration=8 Number of iteration=12 OSM parameter =0.84 OSM parameter =0.64 OSM parameter =0.67 Range Step Range Step Range Step Diffusion 0-100 25 25-75 10 25-35 5 Breed 0-100 25 1-50 10 1-41 5 Spread 0-100 25 1-50 10 1-11 5 Slope 0-100 25 25-95 10 35-85 10 Road Gravity 0-100 25 25-100 10 35-95 10 7.2. URBAN GROWTH PREDICTION BY 2040: The entire prediction phase requires two steps namely: forecast and prediction. These sub-divisions are described below: 7.2.1. FORECASTING COEFFICIENT RUN: After the execution of the calibration phase, the generated values in the ‘control_stats.log ’ file have been used in the preparation of the scenario file for ‘Forecast Run ’ in the prediction mode. In this process, the best set of parameters is utilized to simulate future urban growth and compare it to actual control data (Dietzel & Clarke, 2004 ). In this stage, the step values of each coefficient are set to 1 with stop and start values set to the same. The results of the final calibration and the adopted values of start, step and stop with Monte Carlo iteration of 100 best-fit for predictions are presented in Table 3 . Table 3 Parameters assessing the ‘forecast_run’ for a scenario file Diffusion Breed Spread Slope Road Gravity Start Value 35 41 2 85 95 Stop Value 35 41 2 85 95 Step Value 1 1 1 1 1 To initialise ‘ Forecast_run ’, the best growth coefficient values in the scenario are used to run the SLEUTH model for the historical time. To use ‘avg.log file’ as an outcome, for the next prediction mode (Fig. 7 ). 7.2.2. URBAN GROWTH PREDICTION RUN: The scenario file used to derive ‘Forecast_Run’ is modified as the scenario, used for the next step called the ‘Prediction_Run’ mode of the model. The adopted Monte Carlo Iterations is set to ≥ 100. The calculated coefficient during ‘Forecast_Run’ in the model is used as input and the year of the last urban data layer 2016 is set as the start date and 2040 as the end date in the scenario of ‘Prediction_Run’ (Table 4 ). Table 4 Parameters assessing the ‘prediction_run’ for a scenario_file Best_fit prediction values derived from the forecast run Prediction_Diffusion_Best_Fit 6 Prediction_Breed_Best_Fit 23 Prediction_Spread_Best_Fit 35 Prediction_Slope_Best_Fit 64 Prediction_Road_Best_Fit 18 Prediction Date Range Prediction_Start_Date 2016 Prediction_Stop_Date 2040 As a result of ‘prediction_run’ , an avg.log file, coefficient, memory, and log files carrying significant statistical information are created as an output. In addition, the urban growth maps from 2017 through 2040 are also produced. Figure 8 shows the parametric arrangement of ‘avg.log’ file with automated ( sng, og, rt, pop, area, edges, clusters, rad, slope, difus, spread, breed, rd_grav, %urban and grw_pix ) and manually calculated ( grw_rate and grw_sq. km ) files. 7.3. PREDICTION OUTPUT: The implementation of the model is divided into three phases: testing, calibration, and prediction. The parameterization and calibration of the model were discussed in previous sections. The results of the prediction phase of the SLEUTH model are shown below. The entire study concludes with a prediction of urban growth in the Asan watershed for 2040. The prediction process uses the best set of calibrated parameters to simulate future urban growth. The model generates coloured growth probability pixels for each year. These pixels are overlapped to create a comprehensive final output map within the ArcGIS domain (Fig. 9 ). In addition, an average file is produced as part of the output, containing significant statistical measures for ‘prediction_run’ which is briefly described in Table 5 . Based on Table 5 , the statistic coefficient of ‘avg.log’ file of prediction run, the four coefficients measured i.e., spread, breed, diffusion and road gravity are increasing through the years till 2040. The slope coefficient decreased to 0.32 from 0.42 in 2040 because the critical high slope value is set up to 0.92 instead of 1.34 which is considered in the last two scenario files. This will help us to see more development, especially in the steeper terrain. The ‘sng’ coefficient increased continuously indicating an increase in urban development in previously undeveloped areas whereas the increment in the value of ‘og’ indicates the existing urban cells are expanding into their surroundings. The ‘rt’ also measures a continuous increment in its values throughout the years as a road network is one of the means of promoting urban development. The ‘pop’ coefficient is related to urban growth pixels. It is continuously increasing from 1157202.09 to 2223170.27. Each pixel dimension is 20m * 20m (400m 2 ). In Table 5 , the last column indicates the growing urban extent for each year. Table 5 Description of parameters and comparison of statistical results (of pixels) for 2016 and 2040 Parameter Description 2016 2040 sng Cumulative number of urbanised pixels by neighbourhood growth 4.94 5.90 og Cumulative number of urbanised pixels by organic growth 785.54 2017.35 rt Cumulative number of urbanised pixels by road influenced the growth 14.11 28.11 pop Total number of urban pixels 1157202.09 2223170.27 area Total number of urban pixels 1157202.09 2223170.27 edges Number of urban to non-urban pixel 10777.91 15751.41 clusters Number of urban pixel clusters 147.23 136.34 rad Radius of the surface encloses the urban area 607.92 643.98 slope Slope coefficient 0.42 0.32 diffusion Diffusion coefficient 5.72 6.40 spread Spread coefficient 34.46 45.00 breed Breed coefficient 22.66 23.00 rd_grav Road gravity value 17.98 36.12 %urban Percent of urbanised pixels divided by the number of pixels available for urbanization 22.9 55.82 grw_rate Number of growth pixels divided by the total number of number pixels multiplied by 100 7.88 7.62 grw_pix Number of growth pixels for each year 91155.88 169327.64 grw _sq km Growth pixels multiply by one-pixel dimension 36.46 km 2 67.73 km 2 Figure 10 shows the graphical representation of the growth rate ( grw_rate ) of predicting values from 2017 to 2040. Based on the model, the growth rate till 2016 is 7.88 whereas for 2040 it will be 7.62. It can be analysed that the minimum and maximum growth rates are 7.06 in 2028 and 8.78 in 2038 respectively. 7.4. MODEL VALIDATION: The validation of the model has been conducted by comparing to observed areal statistics of the urban area (derived from the decadal LULCs data) with the simulated/modelled data to validate the model for better accuracy (Table 6 ). This comparison examines whether the SLEUTH model can procreate practically a similar urban extent to that of historical years. Observed vs. Simulated areal statistics: 1980–1995 The observed data show a 2.84 km 2 increase in urban area, whereas simulation data indicate a 3.43 km 2 increase. 1995–2008 The observed increase in urban area is 12.52 km 2 , whereas simulation data indicate a 12 km 2 increase. 2008 to 2016 The observed data report an increase of 11.59 km 2 , whereas simulated data show an 11.48 increase. To enhance the accuracy of the model, a suitable adjustment in the input parameters was made to minimize the differences between the observed and simulated values. This iterative adjustment process ensures that the model’s performance nearly matches the observed data values on an annual basis, thus improving the reliability of future urban growth prediction. Table 6 Comparing the statistical results from 1980 to 2040 Observed Areal Statistics Simulated Areal Statistics Year Urban Area (km 2 ) Urban area Change (%) Increase (km 2 ) Urban Area (km 2 ) Urban area Change (%) Increase (km 2 ) Till 1980 9.55 11.45 - 9.55 6.29 - 1980–1995 12.39 14.86 2.84 12.98 8.55 3.43 1995–2008 24.91 29.88 12.52 24.98 16.46 12.00 2008–2016 36.51 45.79 11.59 36.46 24.03 11.48 2016–2040 - - - 67.73 44.64 31.27 A scatterplot of these areal statistics illustrates the minor variance between the simulated and observed values (Fig. 11 ). The R 2 criterion, with a value of 0.99 for urban growth until 2016, suggests that the model provides satisfactory results when compared to the observed data, indicating high model accuracy and validation. Figure 12 , shows simulated urban areas from 1980 to 2040. The results indicate that, if the current urban growth pattern continues, the urban area will occupy approximately 9.55% of the total area of the watershed by 2040. This represents an increase of 4.33% compared to the urban area in 2016. 8. CONCLUSION Deforestation especially in developing countries is primarily caused by intensive agriculture and urbanisation. Effective planning measures should be used to limit the conversion of forest and agricultural lands into settlements. The paper deals with the prediction of future urban growth by using the CA-based SLEUTH model in the Asan watershed, Uttarakhand, India until 2040 which provides relevant details to the planners to implement strategies related to sustainable development. The entire set of the model’s simulation works in three stages: test, calibration and prediction. The model was calibrated in three phases: coarse, fine, and final using input data of varying resolutions 80m, 40m, and 20m, respectively to determine the optimal growth coefficient values. The prediction phase involved two steps: a forecast run to simulate historical urban growth using the best-fit coefficients, and a prediction run to forecast future growth from 2016 to 2040. The prediction run generated urban growth maps for each year, from 2017 to 2040, providing valuable insights into the spatial patterns and extent of future urbanization in the study area. Based on the predicted urban growth maps of the study area, show that the watershed primarily expanded around the old urban areas due to organic growth. In 2016, the total urban area in the watershed was calculated as 36.5 km 2 and it will be increased to 67.73 km 2 in 2040 based on the result generated from the simulation model. The difference in the urban area between these two years is 31.27 km 2 . The increase in urban percentage is obvious 22.9% in 2016 and 55.82% in 2040. Also, by 2040, the urban settlement would occupy around 9.55% of the total area of the watershed which is 4.33% more than the urban area observed in 2016. The growth rate in 2025 shows a decreasing trend maybe because of the maximum attention given to the conservation of natural resources, agriculture and forest areas. As observed, the dominant direction of urbanization in the last two decades (2008 & 2016) was eastwards which accounted for 35.27% of the total urban area expansion. Based on the model simulation, this dominance is continuing towards the south-eastern direction. Although, as already discussed, the majority of the expansion occurs in and around old urban areas. After 2030, the central and western reaches of the study area will be seen with more growth. According to the urban growth control model, the finding concludes with the fact that the southeastern part of the area has more development potential. The overall study suggests that the urban growth rate in the watershed should be slowed and the spatial pattern of growth should be altered while maintaining sustainable development. References Abd-Allah, M. A., & Mohamed, M. (2007). Modelling urban dynamics using geographic information systems, remote sensing and urban growth models. Faculty of Engineering at Cairo University In . Partial Fulfilment of the Requirements for the degree of doctor of philosophy in architecture faculty of Engineering, Cairo University, Giza. Burdett, M. (2018). 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Garg, V., Khwanchanok, A., Gupta, P. K., Aggarwal, S. P., Kiriwongwattana, K., Thakur, P. K., & Nikam, B. R. (2012). Urbanisation effect on hydrological response: a case study of Asan River watershed, India. Journal of Environment and Earth Science , 2 (9), 39-50. Gilbert, K. M., & Shi, Y. (2023). Urban Growth Monitoring and Prediction Using Remote Sensing Urban Monitoring Indices Approach and Integrating CA-Markov Model: A Case Study of Lagos City, Nigeria. Sustainability , 16 (1), 30. Goldstein, N. C. (2004). Brains versus brawn-comparative strategies for the calibration of a cellular automata-based urban growth model. In Geo-Dynamics (pp. 249-272). CRC Press. Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: measurement, analysis and modelling. Remote Sensing of Environment , 86 (3), 286-302. Jantz, C. A., Goetz, S. J., Donato, D., & Claggett, P. (2010). Designing and implementing a regional urban modelling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems , 34 (1), 1-16. Jat, M. K., Choudhary, M., & Saxena, A. (2017). Application of geo-spatial techniques and cellular automata for modelling urban growth of a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Science, 20 , 223–241. Joshi, B. (2018). Recent Trends of Rural out-migration and its socio-economics and environmental impacts in Uttarakhand Himalaya. Journal of Urban and Regional Studies on Contemporary India. 4(2): 1-14. KantaKumar, L. N., Sawant, N. G., & Kumar, S. (2011). Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area. International Journal of Geomatics and Geosciences , 2 (2), 568. Merryweather‐Clarke, A. T., Liu, Y. T., Shearman, J. D., Pointon, J. J., & Robson, K. J. (1997). A rapid non‐invasive method for the detection of the haemochromatosis C282Y mutation. British Journal of Haematology , 99 (2), 460-463. Onsted, J., & Clarke, K. C. (2012). The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH. International Journal of Geographical Information Science , 26 (5), 881-898. Pettit, C., Shyy, T. K., & Stimson, R. (2002). An on-line planning support system to evaluate urban and regional planning scenarios. In Planning support systems in practice (pp. 331-347). Berlin, Heidelberg: Springer Berlin Heidelberg. Saxena, A., & Jat, M. K. (2019). Capturing heterogeneous urban growth using the SLEUTH model. Remote Sensing Applications: Society and Environment, 13 , 426–434. Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems , 26 (6), 525-552. Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems , 26 (6), 525-552. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management , 30 , 391-405. Xian, G., & Crane, M. (2005). Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment , 97 (2), 203-215. Yifru, B. A., Chung, I. M., Kim, M. G., & Chang, S. W. (2022). Augmenting freshwater availability in mountain headwater streams: Assessing the sustainability under baseline and future climate change scenarios. International Soil and Water Conservation Research , 10 (2), 293-307. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-4598195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315778806,"identity":"55947795-cbbf-4271-b732-3b5c1a01c8f0","order_by":0,"name":"Ankita Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACNoYDcPYHEJ+dGC0wPYwzQHxmIrTArWHmAZMENPDPbks8/KGGwa6fvffxZ5tf2+T5mBkYP3zMwa1F4s6xAwcOHGNIntlz3Ew6t++2YRszA7PkzG14rLmR3nDgABtDssGNNDbm3J7bjEAtbMy8eLTIg7X8Y0i2v5HG/Nmy57Y9QS1Aww8cONjGYGcgkcYgzfDjdiJBLYY30hIOnO2TSJA4c4xNsrfhdnIbM2MzXr/I3Ugz/lDxzcaev72N+cOPP7dt57c3H/zwEZ/3IUAisQFEMbaByQaC6kHAHkL9IUrxKBgFo2AUjDAAAJZ7U5/xSBr+AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9411-4986","institution":"Department of Geology, HNBGU, Srinagar 246174, Uttarakhand, India","correspondingAuthor":true,"prefix":"","firstName":"Ankita","middleName":"","lastName":"Sharma","suffix":""},{"id":315778807,"identity":"f4ba4470-2565-425e-a5d3-9f49aba0c5b5","order_by":1,"name":"Radha Krishan","email":"","orcid":"","institution":"Department of Civil Engineering, Government Engineering College, Banka 813102, Bihar, India","correspondingAuthor":false,"prefix":"","firstName":"Radha","middleName":"","lastName":"Krishan","suffix":""},{"id":315778808,"identity":"144b8a59-cefe-4383-bfa3-0b7f8452f3a8","order_by":2,"name":"Bhaskar Nikam","email":"","orcid":"","institution":"Deputy Director, EDPO, ISRO, Bangalore, 560096, Karnataka, India","correspondingAuthor":false,"prefix":"","firstName":"Bhaskar","middleName":"","lastName":"Nikam","suffix":""},{"id":315778809,"identity":"c7fe0639-b93f-4bce-b9da-4ed12245be83","order_by":3,"name":"Dhirendra Singh Bagri","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dhirendra","middleName":"Singh","lastName":"Bagri","suffix":""}],"badges":[],"createdAt":"2024-06-18 07:45:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4598195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58673868,"identity":"b34f8f34-3a1f-48cd-b1c1-cba76fd62f1d","added_by":"auto","created_at":"2024-06-19 15:22:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":871070,"visible":true,"origin":"","legend":"\u003cp\u003ea: Location of the study area;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb: Urban representation for 1980 and 1995 combined in one map, and for 2008 and 2016 combined in another (to enhance visibility and clarity of land cover changes)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/3e081e0214c1146f1a6d1305.png"},{"id":58673106,"identity":"dd71da79-1891-4a15-bd0b-622d2ed02bff","added_by":"auto","created_at":"2024-06-19 15:14:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517151,"visible":true,"origin":"","legend":"\u003cp\u003eScenario file used in the model calibration shows different coefficients controlling growth types\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/459bf1d359cfa5822f51483f.png"},{"id":58674766,"identity":"b389547d-de4c-4e67-a38f-5b2c369318c1","added_by":"auto","created_at":"2024-06-19 15:30:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253928,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology framework followed in the present chapter\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/8b850ece201583570109af2e.png"},{"id":58673869,"identity":"89aa7b09-c790-4b1c-97db-37b028a1c5ef","added_by":"auto","created_at":"2024-06-19 15:22:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":301981,"visible":true,"origin":"","legend":"\u003cp\u003eLand use/Land cover map of the study area for 2008 \u0026amp; 2016\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eSource\u003c/em\u003e: Sharma \u0026amp; Bagri, 2020, 2023; Sharma \u003cem\u003eet al. \u003c/em\u003e2021)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/d997a87e3c4c61361a767745.png"},{"id":58673108,"identity":"00ca6d30-d1e3-4f68-806d-10e772fa3268","added_by":"auto","created_at":"2024-06-19 15:14:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63260,"visible":true,"origin":"","legend":"\u003cp\u003eInput data layer of urban class\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/52b29a053dab5761f8cc4aa8.png"},{"id":58673110,"identity":"b4d37ed2-3f98-462d-84e2-d770c30af71d","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":735961,"visible":true,"origin":"","legend":"\u003cp\u003eInput data layers of slope, hillshade and road network\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/08ca1d9d89dbec53d1503923.png"},{"id":58673113,"identity":"6a1e10c3-af6b-42a9-bb5a-58c3fc514e57","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":38610,"visible":true,"origin":"","legend":"\u003cp\u003eInput data layer of exclusion (waterbodies)\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/45f0752a583cb21ff338f67b.png"},{"id":58673115,"identity":"13b2712b-c67f-433f-9d99-67161ab0fbba","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":516787,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical growth coefficient of coarse calibration phase represents the different parameters with calculated OSM values arranged in descending order\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eLee-Sallee metric is used for sorting the values to identify the best run\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/1e6a092244a39dd245c0b438.png"},{"id":58673104,"identity":"8f4b7063-29ca-4ac9-af05-c4cb207b103d","added_by":"auto","created_at":"2024-06-19 15:14:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":111410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7:\u003c/strong\u003e \u003cem\u003e‘avg.log file’\u003c/em\u003e used in the \u003cem\u003eforecast_run\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/7aa5175b354a20d08c4ddf7c.png"},{"id":58673117,"identity":"9a41b044-df75-47f4-a16c-aeed213cd876","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":657554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8:\u003c/strong\u003e \u003cem\u003e‘avg.log file’\u003c/em\u003e as output with the statistical measures from 2017 through 2040 for the ‘prediction_run’\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/edcbf8e23da243d1e7c59252.png"},{"id":58673118,"identity":"7f348a80-a3f5-4e0d-b438-31f2d9b6aafb","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":791591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9:\u003c/strong\u003e Urban growth prediction for 2040\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/1ce0a7cd3515355dd0697716.png"},{"id":58673105,"identity":"9509a764-84bd-4141-98f6-e2f47d902882","added_by":"auto","created_at":"2024-06-19 15:14:06","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":63393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10:\u003c/strong\u003e Graphical depiction of growth rates throughout the predicted years (2016-2040)\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/5a0cc52c91e3c23fb4c7a96c.png"},{"id":58673116,"identity":"a7e49b4d-18a8-4d70-a6d9-73a9af898c09","added_by":"auto","created_at":"2024-06-19 15:14:07","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":85415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 11:\u003c/strong\u003e Graphical representation of the simulated and observed urban areal comparison\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/41db3ebd68e7dd1b76e346ef.png"},{"id":58673109,"identity":"a38610cf-7d82-47c8-a313-c2288b6877b1","added_by":"auto","created_at":"2024-06-19 15:14:06","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":86139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 12: \u003c/strong\u003eGraphical representation of the urban area from 1980 through 2040\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/c40c8a07e50ecd187fdf15d8.png"},{"id":58675297,"identity":"01661834-2dcd-43af-b3e2-aabe13ee3f8e","added_by":"auto","created_at":"2024-06-19 15:38:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5589019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598195/v1/bff725e0-ba12-42b8-803c-d6c0a3d822d8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEnhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eMonitoring and assessing urban growth is critical for long-term planning and optimal utilisation of natural resources, also this helps in reducing the risk of issues arising from unplanned urbanisation, such as pollution and environmental disruption. The rapid pace of urbanization and industrialization has led to significant alterations in land use/land cover (LULC) over the last two centuries, which indicates that this pace of alteration will continue to boost in the coming years. Burdett (2012) defines urban growth as \u0026lsquo;the expansion of an area with the number and size of its inhabitants\u0026rsquo;. The desire for facilities, as well as the need for employment, are the primary drivers of urban expansion (Garg, et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, unplanned growth causes a slew of problems such as changing climate, over-exploitation of natural resources, and insufficient infrastructure.\u003c/p\u003e \u003cp\u003eUnplanned land use, poor resource management, poor public policy and funding level are all factors contributing to a lack of understanding of urban dynamics, particularly in developing countries (Xian \u003cem\u003eet al.\u003c/em\u003e 2005). Several LULC change detection methodologies have been introduced recently for monitoring topographical change and urban growth. The radiometric efficiency of the data, the level of uniformity at a particular location and climatic conditions are the modern approaches used to extract LULC maps from multispectral satellite images (Garg, et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban growth modelling is a critical tool in analysing and predicting the dynamics of urban expansion. Silva \u0026amp; Clarke (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) highlight the value of these models in providing policymakers and planners with the ability to anticipate and forecast future changes or trends in urban development. This capability allows them to understand the impacts of future development and explore the potential consequences of different policies (Pettit et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Verburg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The dynamics of urban growth are inherently complex and nonlinear. Urban growth refers to the expansion of towns and cities, typically measured by the increase in the size of built-up areas. This expansion is driven by various factors, including the requirements of the city, available facilities, and the level of industrialization. These factors contribute to the migration of people from rural to urban areas, exerting significant pressure on infrastructure and natural resources. The migration can lead to the formation of slums, increased traffic congestion, the reduction of farmlands, deforestation and threats to wildlife. Understanding these dynamics through modelling can help manage the negative impacts of urban growth. Effective urban growth models enable planners to make informed decisions that balance development needs with environmental and social considerations, promoting sustainable urbanisation.\u003c/p\u003e \u003cp\u003eVarious models have been developed to analyse the LULC change over time and forecast the development of future classes of landuse. These models are based on different modelling approaches and can be categorized as follows: Empirical Statistical Model, Stochastic Model, Optimization Models, Dynamic Process-Based Simulation Model, Cellular Automata Model and Integrated Model. Cellular Automata (CA) models are particularly popular in LULC change analysis for complex regions. These models are based on cellular grids where each grid cell represents a single state at a particular time point. The state change or transition depends on predefined rules. CA-based models can produce spatio-temporal effects with visual interpretations, indicating where the change is taking place. This approach is more popular in LULC change analysis for complex regions and can also be used for long-term prediction. One of the CA-based models widely used for urban growth simulation is the SLEUTH model. This model has been applied in developed countries to simulate urban growth and create urban growth models (Silva \u0026amp; Clarke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dietzel \u0026amp; Clarke \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Onsted \u0026amp; Clarke, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, limited studies have been conducted in developing countries for urban growth simulation (Gilbert \u0026amp; Shi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In India, researchers have made efforts to simulate urban growth models for cities such as Pune, Hyderabad, Pushkar, and Ajmer (Gandhi \u0026amp; Suresh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; KantaKumar et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jat et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saxena \u003cem\u003eet al.\u003c/em\u003e 2019).\u003c/p\u003e \u003cp\u003eIn the current study, the focus of the study is on predicting and simulating urban growth in the Asan watershed, Uttarakhand, India, until 2040, and assessing its impact on the surrounding areas using the CA-based SLEUTH model. The research utilizes a primary dataset spanning the last 36 years (1980\u0026ndash;2016), including DEM and LULC. This multi-decade analysis reveals the gradual expansion of urban areas within the study area. Building on this historical context, the study aims to forecast future urban expansion, providing valuable insights for infrastructure development modelling and planning.\u003c/p\u003e \u003cp\u003eDifferent physical processes such as deforestation, siltation, and mass wasting significantly impact the hydrology of the study area at different scales (Joshi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Dehradun, a city predominantly engaged in administrative, educational, commercial, defence and tourism sectors, is now rapidly transforming into an industrial and major service centre for the entire region.\u003c/p\u003e \u003cp\u003eThe watershed is primarily sustained by monsoonal rainfall and spring drains across the Siwalik range with fragile rocks. Degradation of natural resources, as well as the soaring population, is the major concern of the watershed. Over time, changes in LULC have profoundly altered the hydrological dynamics within the watershed.\u003c/p\u003e \u003cp\u003eForests and agricultural lands from the Siwalik and Lesser Himalayas dominate land use in the study area. Following the establishment of Dehradun as the capital city, the region has experienced rapid population growth, which has markedly affected the green cover. The conversion of rural, forest or wetland areas into urban infrastructure such as streets, highways, institutional and commercial systems has changed the conditions of hydrologic runoff. Human activities have significantly influenced surface hydrology by increasing impervious surfaces and altering land use patterns (Yifru, 2022).\u003c/p\u003e \u003cp\u003eBy considering all the literature reviews and research-based facts, we consider these objectives for our study: (i) Prediction of future urban growth patterns with the help of associated model in terms of geo-environmental aspects and impact of future land use on resource availability (ii) Use historical datasets to track changes in land cover and evaluate these changes through modelling technique.\u003c/p\u003e"},{"header":"2. STUDY AREA","content":"\u003cp\u003eThe Asan River watershed is located in Dehradun district, Uttarakhand, India, encompassing latitudes 30\u003csup\u003e\u0026deg;\u003c/sup\u003e14' 14'' N to 30\u003csup\u003e\u0026deg;\u003c/sup\u003e 29' 54'' N and longitudes 77\u003csup\u003e\u0026deg;\u003c/sup\u003e 39' 42'' E to 78\u003csup\u003e\u0026deg;\u003c/sup\u003e 05' 30'' E. The area spans elevations from 345m to 2188m above mean sea level (MSL) and covers an area of approximately 712.34 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The watershed extends for about 40 km in length, with an approximate widest width of 18 km. Situated in Doon Valley at the foothills of the Siwalik ranges, the Asan River flows centrally from the northeast to the southwest. As the river moves toward the southwest, it carries water from several streams flowing southwards from the Lesser Himalayan range and northwards from the Siwaliks. Asan River is the tributary of Yamuna River having an approximate length of 43 km within the watershed\u0026rsquo;s boundary. In the lower reaches, a relatively flat section flows that feeds the Asan barrage. It is located in the vicinity of the Asan and Yamuna Rivers. The water from the barrage later drains into the Yamuna at Paonta Sahib.\u003c/p\u003e"},{"header":"3. DESCRIPTION OF SLEUTH MODEL","content":"\u003cp\u003eThe SLEUTH model, developed by Clarke \u0026amp; Gaydos (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and later refined by Silva \u0026amp; Clarke (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), uses several key layers to simulate urban growth: Slope, Land Use/Land Cover, Exclusion, Urbanization, Transportation (Road), and Hillshade. These components form the acronym SLEUTH and are essential for generating accurate urban growth predictions. This Cellular Automata (CA)-based\u003c/p\u003e \u003cp\u003eModels consist of two modules: the Clarke Urban Growth Model (UGM) which is used to simulate urban growth and the Land Cover Deltatron Model (LCD), used to simulate land-use change and land transition. The SLEUTH model operates on a nested loop and is written in the \u0026lsquo;C\u0026rsquo; language. The inner loop of the model performs growth rules for a single year and the outer loop implements every growth history repeatedly and stores information about all the cumulative statistical data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The input layer of the urban growth is extracted from the decadal LULC maps of the study area. The growth rule which includes the coefficient values, spreading growth type, number of iterations, topographic slope and proximity to the road network, determines the potential urbanization of cells. These rules result in the expansion of the urban layer in the following year, forming a modified array that reflects the new urban extent (Merryweather-Clarke et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Clarke \u0026amp; Gaydos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This comprehensive model allows for detailed simulation and analysis of urban growth patterns, providing valuable insights for urban planning and development. In the model, five factors that control the behaviour of the system are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBreed factor coefficient\u003c/b\u003e: It determines the probability of new spreading centres and the growth of settlements influenced by roads.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiffusion factor coefficient\u003c/b\u003e: It controls the overall spontaneous growth influenced by roads and the search distance along the road network.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSpread Coefficient\u003c/b\u003e: It controls the diffusion expansion/organic growth that occurs from established urban pixels.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSlope resistance\u003c/b\u003e: It influences the slope of urban pixels; as the degree of slope increases, the likelihood of settlement decreases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRoad gravity\u003c/b\u003e: It controls the outward distance at which new urban pixels develop due to road-influenced growth.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"4. METHODOLOGY","content":"\u003cp\u003eThe CA-based urban growth SLEUTH model is evaluated with input layers of three different resolutions coarse, medium, and fine in three phases of calibration namely: coarse, fine and final, respectively. Input data preparation, model calibration, model prediction, and model output are the four major steps in the SLEUTH model execution. Three scenario files are edited by changing the input layers of the adopted resolution, and then independently calibrated and predicted. We used the SLEUTH 3.0 beta p01_module (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncgia.ucsb.edu/projects/gig/\u003c/span\u003e\u003cspan address=\"https://www.ncgia.ucsb.edu/projects/gig/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e and Cygwin software to complete the model compilation and operation. The detailed methodology in the form of a flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e"},{"header":"5. INPUT DATA PARAMETERIZATION","content":"\u003cp\u003eThe execution of the SLEUTH model requires six types of input maps as mentioned in the acronym SLEUTH and these are Slope, Land Use/Land Cover, Exclusion, Urban, Transportation and Hillshade of different years. The initiation requires slope maps, two decadal LULC maps, one exclusion layer (which includes any LULC class other than urban), urban (built-up) maps for four years, a transportation map for two years, one hillshade map for observing the topography and two decadal LULC maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The LULC maps for 2008 and 2016 were created using our previous research, which classified eight categories: built-up areas (urban and rural), cropland, fallow land, plantation, evergreen needle leaf forest, deciduous broadleaf forest, mixed forest, and water bodies. These maps were manually digitized through visual interpretation for two different years using Landsat 5 (TM) and LISS-IV imagery (Sharma \u0026amp; Bagri, 2020, 2023; Sharma \u003cem\u003eet al.\u003c/em\u003e 2021). In ArcGIS, the road layers initially digitized are transformed into a raster format. To suit the requirements of the model, these road layers need to be represented as binary images. Consequently, pixels corresponding to non-road areas are coded as zero, while those representing roads are coded as one. Similarly, areas that are excluded from the analysis are coded as one, while non-excluded areas are coded as zero (Herold et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dietzel \u0026amp; Clarke, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; KantaKumar et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urban (built-up) layer is derived from the extraction of features from these maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The slope map (in degrees) and hillshade for topography are prepared from Cartosat-1 (10m) using ArcGIS. By digitising roads from reference maps (satellite images and SOI toposheets), two transportation (road) maps have been created (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). To make an exclusion map, recent waterbodies are extracted from the LULC (2016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In addition, all inputs were resampled into three datasets for three stages of model calibration with different resolutions: coarse (80m), fine (40m), and final (20m), with an 8-bit radiometric resolution. As a result, for each of the three calibration phases, three different row and column sizes are created. All layers (input raster data) must be in a standardised format, which is an 8-bit unsigned GIF.\u003c/p\u003e "},{"header":"6. MODEL EXECUTION","content":"\u003cp\u003eA brief account of three phases of SLEUTH model processing namely:\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e6.1. TEST PHASE:\u003c/h2\u003e \u003cp\u003eThe test phase is used to ensure that the preliminary conditions and input variables are appropriate to meet the standard criteria. It is an initial phase and is critically performed before calibrating the model. After several attempts of a run to validate the criteria, the successful run of the test phase indicates that the desired preliminary conditions required for the model execution have been met, and it is now prepared for the calibration phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e6.2. CALIBRATION PHASE:\u003c/h2\u003e \u003cp\u003eThe calibration is the most important aspect of the urban growth simulation (Dietzel \u0026amp; Clarke, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jat et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The primary goal of this phase is to determine the best growth coefficient combination. In this study, the \u003cem\u003e\u0026ldquo;Brute force parameter estimation\u0026rdquo;\u003c/em\u003e algorithm is used to calibrate the model. Calibration is done in three stages here: coarse, fine, and final. At first, the coarse phase of the model calibration was run which required an input dataset of 80m resolution, the adopted initial values of growth coefficients, and the number of Monte Carlo iterations. Furthermore, the values of the refined growth coefficient were calculated and adopted for other scenario files belonging to the fine (40m) and final (20m) calibration phases based on the computed statistical metrics (\u003cem\u003econtrol_stats\u003c/em\u003e) generated as an output of coarse calibration. The higher the number of Monte Carlo iterations chosen, the finer the spatial resolution of the input dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e6.3. PREDICTION PHASE:\u003c/h2\u003e \u003cp\u003eThe prediction phase involves running the model with the datasets of the finer resolution, a number of Monte Carlo iterations\u0026thinsp;\u0026ge;\u0026thinsp;100 and the best-fit growth coefficient generated during the calibration phase. It's a one-step process that generates prediction images as well as statistical data files storing information about urban growth. The entire setup of the model is prepared to predict future urban growth until 2040.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. RESULTS AND DISCUSSIONS","content":"\u003cp\u003eThis section presents the result of the urban growth SLEUTH model. The selection and usage of input data parameters for different phases were detailed in the previous parameterization section. The model implementation was divided into three phases: test, calibration and prediction.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e7.1. SLEUTH MODEL CALIBRATION:\u003c/h2\u003e \u003cp\u003eIn the calibration phase, there are two methods for estimating model parameters: brute force calibration and genetic algorithm. To find the best coefficient values, the brute force algorithm goes through three stages. On the contrary, the genetic algorithm encompasses an adaptive search of the coefficients space. The calibration phase entails refining growth coefficient values sequentially from one phase to the next (Goldstein, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The main objective of the model calibration is to accurately recreate historical urban expansion as it appears in the input datasets. To find the optimum growth coefficient values, a quantitative value is arranged from all the possible iterations that are the best fit. This phase is a highly complex process that must be computed (Jantz et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Simulation of growth parameters was done several times by setting Monte Carlo iteration and further comparing the simulated and actual growth by determining various information about urban pixels such as their number, size, edge pixel and many others. In the end, the model generates a least square regression metric called \u003cem\u003e\u0026lsquo;control_stats.log\u0026rsquo;\u003c/em\u003e, for each calibration phase (i.e., coarse, fine and final) which stores information about thirteen parameters such as population, mean cluster size, composite score, edges, compare value, average slope, Lee-Salle metric, urban clusters, X-mean, Y-mean and so on. The performance of these parametric combinations is evaluated using these logs (Abd-Allah, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Each metric represents the degree of fit between observed and predicted (modelled) growth. The values of the coefficient range for each calibration phase are determined using a variety of methods. Here in this study Optimum SLEUTH Metric (OSM) method developed by Dietzel \u0026amp; Clarke (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), is used to calculate the values of coefficient space using the formula (Eq.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003eOSM\u0026thinsp;=\u0026thinsp;compare * pop * edges * clusters * slope * xmean * ymean 1\u003c/p\u003e \u003cp\u003eThis formulation assisted in sorting metrics in descending order by allocating weightage to them and then with the top three highest weightage being used to determine the value of coefficient space (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Dietzel \u0026amp; Clarke, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; KantaKumar et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These values are averaged to get derived growth coefficients from each calibration phase, which are further used as start and stop values for subsequent phase runs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Growth coefficients from final calibration are also being used in the prediction phase of urban growth. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, shows the parametric arrangement of generated file \u003cem\u003e\u0026lsquo;control_stats.log\u0026rsquo;\u003c/em\u003e for a coarse phase of calibration. The Lee-Sallee metric is a \u0026ldquo;measure of spatial fit between the modelled urban growth to the known urban extent\u0026rdquo; (Dietzel \u0026amp; Clarke, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). As a result, the Lee-Sallee metric is used as a primary criterion to narrow the coefficient space.\u003c/p\u003e\u003cp\u003eThe parameters of the linear square metric are averaged and considered for three scenarios used in the calibration phases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters assessing the model calibration for the three scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLeast Square Regression Estimates\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScenario 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScenario 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelled final population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of urban pixels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban perimeter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban cluster edge pixels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee-Sallee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShape index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope of known urban cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage of X values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY-mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage of Y values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the evaluation of the calibration phase for three scenarios. The \u003cem\u003e'Compare'\u003c/em\u003e metric values of Scenarios 1 and 3 are 0.95 and 0.94 respectively which is higher than Scenario 2 (0.93). The \u003cem\u003e'Population'\u003c/em\u003e metric values are all above 0.97. The \u003cem\u003e'Edges'\u003c/em\u003e represent a significant relation between the urban edge of actual and simulated years depicting the values 0.99, 0.95 and 0.99 for Scenarios 1, 2 and 3 respectively. Along with it, the \u003cem\u003e'Cluster'\u003c/em\u003e metric values are above 0.83 for every scenario demonstrating the model's ability to accurately simulate the urban shape and urban cluster changes. The \u003cem\u003e'Lee-Sallee'\u003c/em\u003e metric represents 0.97 for scenarios 1 and 3 while 0.96 for scenario 2. The \u0026lsquo;\u003cem\u003eX\u0026rsquo;\u003c/em\u003e \u0026amp; \u003cem\u003e\u0026lsquo;Y\u003c/em\u003e-mean\u0026rsquo; metrics are all above 0.83, indicating a significant relationship between urbanised cells of actual and simulated years. The outcome of metric evaluation shows acceptable simulation effects in terms of the area, shape, cluster, and location of the urban cell and enables us to be assured in using urban prediction by 2040.\u003c/p\u003e \u003cp\u003eTable 2: \u0026nbsp;\u0026nbsp;Coefficient settings for three scenarios of the different calibration phase\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.178861788617887%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoarse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.178861788617887%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.72357723577236%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber of iteration=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.72357723577236%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber of iteration=8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.552845528455286%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber of iteration=12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.72357723577236%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOSM parameter =0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.72357723577236%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOSM parameter =0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.552845528455286%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOSM parameter =0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.853658536585366%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.869918699186993%\" valign=\"top\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eDiffusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e25-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495934959349594%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eBreed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e1-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495934959349594%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e1-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSpread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e1-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495934959349594%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e1-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e25-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495934959349594%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e35-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRoad Gravity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e25-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495934959349594%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e35-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.821138211382113%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e7.2. URBAN GROWTH PREDICTION BY 2040:\u003c/h2\u003e \u003cp\u003eThe entire prediction phase requires two steps namely: forecast and prediction. These sub-divisions are described below:\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e7.2.1. FORECASTING COEFFICIENT RUN:\u003c/h2\u003e \u003cp\u003eAfter the execution of the calibration phase, the generated values in the \u003cem\u003e\u0026lsquo;control_stats.log\u003c/em\u003e\u0026rsquo; file have been used in the preparation of the scenario file for \u003cem\u003e\u0026lsquo;Forecast Run\u003c/em\u003e\u0026rsquo; in the prediction mode. In this process, the best set of parameters is utilized to simulate future urban growth and compare it to actual control data (Dietzel \u0026amp; Clarke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In this stage, the step values of each coefficient are set to 1 with stop and start values set to the same. The results of the final calibration and the adopted values of start, step and stop with Monte Carlo iteration of 100 best-fit for predictions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters assessing the \u003cem\u003e\u0026lsquo;forecast_run\u0026rsquo;\u003c/em\u003e for a scenario file\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiffusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpread\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRoad Gravity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStop Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo initialise \u0026lsquo;\u003cem\u003eForecast_run\u003c/em\u003e\u0026rsquo;, the best growth coefficient values in the scenario are used to run the SLEUTH model for the historical time. To use \u003cem\u003e\u0026lsquo;avg.log file\u0026rsquo;\u003c/em\u003e as an outcome, for the next prediction mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e7.2.2. URBAN GROWTH PREDICTION RUN:\u003c/h2\u003e \u003cp\u003eThe scenario file used to derive \u003cem\u003e\u0026lsquo;Forecast_Run\u0026rsquo;\u003c/em\u003e is modified as the scenario, used for the next step called the \u003cem\u003e\u0026lsquo;Prediction_Run\u0026rsquo;\u003c/em\u003e mode of the model. The adopted Monte Carlo Iterations is set to \u0026ge;\u0026thinsp;100. The calculated coefficient during \u003cem\u003e\u0026lsquo;Forecast_Run\u0026rsquo;\u003c/em\u003e in the model is used as input and the year of the last urban data layer 2016 is set as the start date and 2040 as the end date in the scenario of \u003cem\u003e\u0026lsquo;Prediction_Run\u0026rsquo;\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters assessing the \u003cem\u003e\u0026lsquo;prediction_run\u0026rsquo;\u003c/em\u003e for a scenario_file\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBest_fit prediction values derived from the forecast run\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Diffusion_Best_Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Breed_Best_Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Spread_Best_Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Slope_Best_Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Road_Best_Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrediction Date Range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Start_Date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction_Stop_Date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a result of \u003cem\u003e\u0026lsquo;prediction_run\u0026rsquo;\u003c/em\u003e, an \u003cem\u003eavg.log\u003c/em\u003e file, coefficient, memory, and log files carrying significant statistical information are created as an output. In addition, the urban growth maps from 2017 through 2040 are also produced. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the parametric arrangement of \u003cem\u003e\u0026lsquo;avg.log\u0026rsquo;\u003c/em\u003e file with automated (\u003cem\u003esng, og, rt, pop, area, edges, clusters, rad, slope, difus, spread, breed, rd_grav, %urban and grw_pix\u003c/em\u003e) and manually calculated (\u003cem\u003egrw_rate and grw_sq. km\u003c/em\u003e) files.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e7.3. PREDICTION OUTPUT:\u003c/h2\u003e \u003cp\u003eThe implementation of the model is divided into three phases: testing, calibration, and prediction. The parameterization and calibration of the model were discussed in previous sections. The results of the prediction phase of the SLEUTH model are shown below.\u003c/p\u003e \u003cp\u003eThe entire study concludes with a prediction of urban growth in the Asan watershed for 2040. The prediction process uses the best set of calibrated parameters to simulate future urban growth.\u003c/p\u003e \u003cp\u003eThe model generates coloured growth probability pixels for each year. These pixels are overlapped to create a comprehensive final output map within the ArcGIS domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, an average file is produced as part of the output, containing significant statistical measures for \u003cem\u003e\u0026lsquo;prediction_run\u0026rsquo;\u003c/em\u003e which is briefly described in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBased on Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the statistic coefficient of \u003cem\u003e\u0026lsquo;avg.log\u0026rsquo;\u003c/em\u003e file of prediction run, the four coefficients measured i.e., spread, breed, diffusion and road gravity are increasing through the years till 2040. The slope coefficient decreased to 0.32 from 0.42 in 2040 because the critical high slope value is set up to 0.92 instead of 1.34 which is considered in the last two scenario files. This will help us to see more development, especially in the steeper terrain. The \u003cem\u003e\u0026lsquo;sng\u0026rsquo;\u003c/em\u003e coefficient increased continuously indicating an increase in urban development in previously undeveloped areas whereas the increment in the value of \u003cem\u003e\u0026lsquo;og\u0026rsquo;\u003c/em\u003e indicates the existing urban cells are expanding into their surroundings. The \u003cem\u003e\u0026lsquo;rt\u0026rsquo;\u003c/em\u003e also measures a continuous increment in its values throughout the years as a road network is one of the means of promoting urban development. The \u003cem\u003e\u0026lsquo;pop\u0026rsquo;\u003c/em\u003e coefficient is related to urban growth pixels. It is continuously increasing from 1157202.09 to 2223170.27. Each pixel dimension is 20m * 20m (400m\u003csup\u003e2\u003c/sup\u003e). In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the last column indicates the growing urban extent for each year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of parameters and comparison of statistical results (of pixels) for 2016 and 2040\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2040\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative number of urbanised pixels by neighbourhood growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative number of urbanised pixels by organic growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e785.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2017.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative number of urbanised pixels by road influenced the growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number of urban pixels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1157202.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2223170.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number of urban pixels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1157202.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2223170.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eedges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of urban to non-urban pixel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10777.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15751.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of urban pixel clusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadius of the surface encloses the urban area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e607.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e643.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediffusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiffusion coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003espread\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpread coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreed coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erd_grav\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad gravity value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of urbanised pixels divided by the number of pixels available for urbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrw_rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of growth pixels divided by the total number of number pixels multiplied by 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrw_pix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of growth pixels for each year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91155.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169327.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrw _sq km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth pixels multiply by one-pixel dimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.46 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.73 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the graphical representation of the growth rate (\u003cem\u003egrw_rate\u003c/em\u003e) of predicting values from 2017 to 2040. Based on the model, the growth rate till 2016 is 7.88 whereas for 2040 it will be 7.62. It can be analysed that the minimum and maximum growth rates are 7.06 in 2028 and 8.78 in 2038 respectively.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e7.4. MODEL VALIDATION:\u003c/h2\u003e \u003cp\u003eThe validation of the model has been conducted by comparing to observed areal statistics of the urban area (derived from the decadal LULCs data) with the simulated/modelled data to validate the model for better accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This comparison examines whether the SLEUTH model can procreate practically a similar urban extent to that of historical years.\u003c/p\u003e \u003cp\u003eObserved vs. Simulated areal statistics:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e1980\u0026ndash;1995\u003c/strong\u003e \u003cp\u003eThe observed data show a 2.84 km\u003csup\u003e2\u003c/sup\u003e increase in urban area, whereas simulation data indicate a 3.43 km\u003csup\u003e2\u003c/sup\u003e increase.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e1995\u0026ndash;2008\u003c/strong\u003e \u003cp\u003eThe observed increase in urban area is 12.52 km\u003csup\u003e2\u003c/sup\u003e, whereas simulation data indicate a 12 km\u003csup\u003e2\u003c/sup\u003e increase.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e2008 to 2016\u003c/strong\u003e \u003cp\u003eThe observed data report an increase of 11.59 km\u003csup\u003e2\u003c/sup\u003e, whereas simulated data show an 11.48 increase.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo enhance the accuracy of the model, a suitable adjustment in the input parameters was made to minimize the differences between the observed and simulated values. This iterative adjustment process ensures that the model\u0026rsquo;s performance nearly matches the observed data values on an annual basis, thus improving the reliability of future urban growth prediction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparing the statistical results from 1980 to 2040\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eObserved Areal Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSimulated Areal Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Area\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban area\u003c/p\u003e \u003cp\u003eChange\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban Area\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban area\u003c/p\u003e \u003cp\u003eChange\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTill 1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA scatterplot of these areal statistics illustrates the minor variance between the simulated and observed values (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The R\u003csup\u003e2\u003c/sup\u003e criterion, with a value of 0.99 for urban growth until 2016, suggests that the model provides satisfactory results when compared to the observed data, indicating high model accuracy and validation.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, shows simulated urban areas from 1980 to 2040. The results indicate that, if the current urban growth pattern continues, the urban area will occupy approximately 9.55% of the total area of the watershed by 2040. This represents an increase of 4.33% compared to the urban area in 2016.\u003c/p\u003e\u003c/div\u003e"},{"header":"8. CONCLUSION","content":"\u003cp\u003eDeforestation especially in developing countries is primarily caused by intensive agriculture and urbanisation. Effective planning measures should be used to limit the conversion of forest and agricultural lands into settlements. The paper deals with the prediction of future urban growth by using the CA-based SLEUTH model in the Asan watershed, Uttarakhand, India until 2040 which provides relevant details to the planners to implement strategies related to sustainable development. The entire set of the model\u0026rsquo;s simulation works in three stages: test, calibration and prediction. The model was calibrated in three phases: coarse, fine, and final using input data of varying resolutions 80m, 40m, and 20m, respectively to determine the optimal growth coefficient values. The prediction phase involved two steps: a forecast run to simulate historical urban growth using the best-fit coefficients, and a prediction run to forecast future growth from 2016 to 2040. The prediction run generated urban growth maps for each year, from 2017 to 2040, providing valuable insights into the spatial patterns and extent of future urbanization in the study area.\u003c/p\u003e \u003cp\u003eBased on the predicted urban growth maps of the study area, show that the watershed primarily expanded around the old urban areas due to organic growth. In 2016, the total urban area in the watershed was calculated as 36.5 km\u003csup\u003e2\u003c/sup\u003e and it will be increased to 67.73 km\u003csup\u003e2\u003c/sup\u003e in 2040 based on the result generated from the simulation model. The difference in the urban area between these two years is 31.27 km\u003csup\u003e2\u003c/sup\u003e. The increase in urban percentage is obvious 22.9% in 2016 and 55.82% in 2040. Also, by 2040, the urban settlement would occupy around 9.55% of the total area of the watershed which is 4.33% more than the urban area observed in 2016. The growth rate in 2025 shows a decreasing trend maybe because of the maximum attention given to the conservation of natural resources, agriculture and forest areas. As observed, the dominant direction of urbanization in the last two decades (2008 \u0026amp; 2016) was eastwards which accounted for 35.27% of the total urban area expansion. Based on the model simulation, this dominance is continuing towards the south-eastern direction. Although, as already discussed, the majority of the expansion occurs in and around old urban areas. After 2030, the central and western reaches of the study area will be seen with more growth. According to the urban growth control model, the finding concludes with the fact that the southeastern part of the area has more development potential. The overall study suggests that the urban growth rate in the watershed should be slowed and the spatial pattern of growth should be altered while maintaining sustainable development.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbd-Allah, M. A., \u0026amp; Mohamed, M. (2007). Modelling urban dynamics using geographic information systems, remote sensing and urban growth models. \u003cem\u003eFaculty of Engineering at Cairo University In\u003c/em\u003e. Partial Fulfilment of the Requirements for the degree of doctor of philosophy in architecture faculty of Engineering, Cairo University, Giza.\u003c/li\u003e\n \u003cli\u003eBurdett, M. (2018). Urban growth and Urbanization,\u0026nbsp;\u003cem\u003eGeographyCaseStudy.Com\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eClarke, K. C., \u0026amp; Gaydos, L. J. (1998). 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Augmenting freshwater availability in mountain headwater streams: Assessing the sustainability under baseline and future climate change scenarios. \u003cem\u003eInternational Soil and Water Conservation Research\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 293-307.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban growth assessment, Urban dynamics, Growth patterns, calibration \u0026 prediction, LULC, SLEUTH model, Sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-4598195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban growth monitoring and assessment are crucial for sustainable long-term planning and the efficient utilization of natural resources. Unplanned urbanization poses risks such as pollution and environmental disruption, emphasizing the need for proactive management. Changes in Land Use and Land Cover (LULC) with time indicate ongoing urbanization trends. This study focuses on predicting and simulating urban growth in the Asan watershed, Uttarakhand, India until 2040 using the SLEUTH model and assessing the impact on the surrounding areas. The research utilizes primary datasets from 1980 to 2016, including the Digital Elevation Model (DEM) and LULC, to forecast future urban expansion. The SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, based on Cellular Automata (CA) principles, is employed to simulate urban growth by analysing and projecting LULC changes from 2016 to 2040. Recent methodologies prioritize the detection of LULC changes through multispectral satellite images, emphasizing factors like radiometric efficiency, spatial uniformity, and climatic conditions. The predicted urban growth output revealed that the projected increase of urban area by 2040 will be 67.73 km\u0026sup2; from 36.5 km\u0026sup2; in 2016 with an increment of 31.23 km\u0026sup2;. Additionally, by 2040, urban settlement is expected to occupy around 9.5% of the total watershed area, an increase of 4.3% from the urban area observed in 2016. The study aims to guide infrastructure planning and promote sustainable development practices by comprehending urban dynamics, growth patterns, and resource management.\u003c/p\u003e","manuscriptTitle":"Enhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 15:13:59","doi":"10.21203/rs.3.rs-4598195/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cede8a6-b266-47a9-a59d-1b3df44a30af","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33458389,"name":"Urban Studies"}],"tags":[],"updatedAt":"2024-09-06T11:48:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 15:13:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598195","identity":"rs-4598195","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}