Modelling landuse dynamics of ecologically sensitive peri-urban space by incorporating an ANN cellular automata-Markov model for Siliguri urban agglomeration, India

Numerous cities throughout the world are experiencing tremendous population growth in their peripheral areas, resulting in a progressive modification of landscapes and raising serious concerns about natural environments, notably forests and agricultural area. Monitoring LULC changes can assist in understanding historical trends, while simulation-based modelling shed light on possible potential future developments. Both of these tactics are indispensable and complimentary for implementing effective land use policies to mitigate the adverse ramifications of urbanization. Present area of investigation, Siliguri town one of prime trading hub of whole north-east India surrounded by ecologically sensitives zones Himalayas. To monitor land use dynamics of peri-urban spaces in Siliguri town Landsat images of 2000, 2010 and 2020 were derived from USGS and classified using Support vector machine learning algorithms. Following the quantification of the previous trend of landuse change, an integrated Artificial Neural Network (ANN) and CA-Markov chain Model was utilized to forecast LULC for the years 2030 and 2050. Eleven pertinent geographical factors, comprising topographical, socioeconomic, and connectivity information, were generated and validated using the crammer v test. The results from LULC modeling predicts as compared to 2020, the urban area is expected to increase by 48.23%, while forest areas, other vegetation cover, and agricultural areas are predicted to shrink by 9.42%, 29.83%, and 26.60% respectively, by the year 2050. The results could provide useful information about historical and potential landuse change and as well as assist local governments in formulating management strategies for the protection of ecological resource.