Comparative framework for spatially explicit urban growth modeling for monitoring urban land-use efficiency and sustainable urban development (SDG 11.3.1): a study on Kolkata metropolitan area, India

The present study attempts to model the spatiotemporal urban growth of Kolkata metropolitan area (KMA), India, in a comparative modeling framework using three (remote sensing and geographic information system) RS-GIS integrated techniques, namely stochastic-choice Markov-chain (STCHOICE), cellular automata-Markov (CA-MARKOV), multi-layer perceptron neural network (MLPNN) coupled with approaches intending monitor land-use efficiency defined by United Nations for sustainable development KMA. In order find out best approach, each techniques is engaged KMA’s growth, thus obtained employed predict future growth. MLPNN (Kappa = 0.9025) appears be considerably better approach as compared other two approaches: CA-MARKOV 0.6941) STCHOICE 0.5392). simulated 2036 reveals that built-up cover expected about 55% from 31% 2016 due significant conversion land covers. peri-urban areas do not have similar pattern consumption central KMA reflects compact periphery reflecting leapfrog result rapid sprawling. HIGHLIGHTSPredicted Metropolitan Area (KMA) frameworkMonitored use (SDG 11.3.1) KMAThe are differentIdentified most influential driving factors selected responsible spatial expansion