Suchergebnisse

Unplanned urbanization would pose serious threats to both environment and mankind. Hence, urbangrowth model (UGM) becomes mandatory to predict future growth of a city. In the current study, urbangrowth of Sriperumbudur Taluk, Tamil Nadu, India was predicted using three types of Cellular Automata(CA) model namely Traditional CA (TCA) model, Agents based Cellular Automata (ACA) Model and NeuralNetwork coupled Agents- based Cellular Automata (NNACA) model. The urban maps of the study regionfor the years 2009, 2013 and 2016 along with the influencing agents of urbanization namely transporta-tion, industries, elevation and also hotspot locations based on the Government policy were used in themodeling. Analytical Hierarchical Process (AHP) technique was adopted to estimate the weights of theagents for suitability map preparation in ACA model. On validating 2016 predicted outputs, NNACAmodel proved to be the better urban model (kappa coefficient - 0.72) when compared to TCA and ACAmodels (kappa coefficient - 0.6 each). Shannon's entropy measure revealed that the urbanization is con-centrated in the north-east direction and it is predicted to have an urban sprawl area of 157 km2in 2020using NNACA model while the observed urbanization is 113 km2of the area in 2016.