Land Use and Land Cover Change (LULC) Analysis and Prediction Using Cellular Automata - Artificial Neural Network (CA-ANN) Approach: A Case Study of Sriperumbudur Taluk, Tamil Nadu, India

India's major metropolitan centres are changing quickly, Sriperumbudur Taluk a peri-urban landscapes are a good example of this shift. It includes industrial growth, expanding transport networks, and residential development are all taking place alongside continuing agricultural activity. This study examines land use and land cover (LULC) change in the taluk between 2016 and 2020 and forecasts future LULC for 2024 and 2028 using a Cellular Automata–Artificial Neural Network (CA-ANN) approach. Sentinel-2A data was classified through supervised maximum likelihood algorithm into five categories: agricultural land, built-up area, sparse vegetation, water bodies, and barren land. The resulting maps obtained an overall accuracy of 84.20%, 86.52%, and 87.12% for 2016, 2020, and 2024 respectively, with corresponding Kappa coefficients of 0.826, 0.847, and 0.861. The MOLUSCE plugin was used to model transition potentials based on the change observed between 2016 and 2020. The elevation, distance from roads, population density, and geomorphology are taken as spatial driving variables. When compared against an independently classified 2024 reference map, the simulated 2024 map achieved a prediction accuracy of 83.72%, and the validated model was subsequently used to project LULC for 2028. Between 2016 and 2028, agricultural land in the study area shows a consistent and substantial decline, while built-up land steadily expands, reflecting sustained urbanization pressure. Water bodies remain relatively stable with minor fluctuation. Spatially, this growth is concentrated along existing transport corridors and around established industrial zones. These findings suggest that CA-ANN-based geospatial modelling can offer useful evidence for urban growth management, land use zoning, and the protection of productive agricultural land in fast-developing peri-urban regions.

Land Use/Land Cover Change, Cellular Automata, Artificial Neural Network, Geospatial Modelling, Urbanization

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