Spatiotemporal analysis and prediction of urban evolution patterns using ANN tool

Abstract

The precise quantification of land-use land cover plays a vital role in preserving sustainability, which is being affected by growing urbanisation. The study proposes the comprehensive Geographical Information System approach in integration with Artificial Neural Network to analyse the past development patterns of a city for predicting future land transformations. In this study, land transformations over the past three decades (1990–2020) 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 2030 using the multi-layer perceptron and cellular automata 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 visualised through the exclusively developed ward-by-ward built-up land density maps. The utilisation of the simulated map in the proposed way helps to prepare comprehensive micro-level urban development planning by incorporating natural resource conservation and land-use planning.