Spatiotemporal analysis and prediction of urban evolution patterns using ANN tool

Abstract

The precise quantification of Land Use Land Cover (LULC) plays a vital role in preserving sustainability, which is being affected by growing urbanization. The study proposes the comprehensive Geographical Information System (GIS) approach in integration with Artificial Neural Network (ANN) to analyse the past development patterns of the city for predicting future land transformations. In the present study, land transformations over the past three decades (for years 1990, 2000, 2010, 2015, and 2020) were analysed using classified maps for Jaipur city, India, as a case study, which reveals that the built-up land was increased by 46.55%. Subsequently, the simulated land transformation map for the year 2030 using the Multi-Layer Perceptron (MLP) and Cellular Automata (CA) anticipates that the built-up land would be increased by 12.68% by cutting down the barren land and vegetation by 9.44% and 3.24%, respectively. The simulation offers strong evidence that most of the medium-built-up land density municipality wards transform into high-density built-up land density wards during the next decade, which is visualized through the exclusively developed ward-by-ward built-up land density maps. The utilization of the simulated map in a proposed way helps to prepare the comprehensive micro-level urban development plan by incorporating natural resource conservation and land use planning.