Modeling Urban Expansion Patterns Through the Application of Geospatial Techniques and Artificial Neural Networks
Abstract
Human impact on land use and land cover (LULC) significantly affects the local and global environment. Accelerating urbanization affects developing nations more than developed nations. Multispectral satellite imagery of 2001, 2011, and 2021 was utilized for urban area transformations due to anthropogenic activities in Assam's Goalpara district. LANDSAT datasets were categorized into four classes: urban areas, vegetation, water bodies, and vacant land. The classification's accuracy was validated using the Semi-Automatic Classification (SCP) tool, which analyzed urban growth in Goalpara District by examining proximity-based factors that influenced its development pattern. A hybrid simulation model, Artificial Neural Network (ANN), was utilized to predict the future urban sprawl for 2031 and 2041 the model validation involved comparing simulated results with the actual data. Predictions reveal 61.6 percent accuracy for the ANN algorithm and 41.8 percent for the Markov Chain algorithm. Urban areas will expand from 91.2sqkm in 2001 to approximately 333.52 sqkm by 2041. These findings highlight integrating remote sensing, GIS, and AI models for urban planning, providing highly accurate insights for effective decision-making.
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