Neural Network Model for Consequence Analysis of Developmental Proposals

Abstract

Application of neural networks for regional system identification is reported in the context of sustainable development. Neural network I captures relationships among natural resource status, economic conditions, amenities, and environmental status; it is used for the prediction of environmental status resulting from the socioeconomic developmental alternatives. Neural network II predicts the quality of life levels. A geographic information system linked with an external relational database system maintained spatial and numerical data, and provided inputs to neural networks. The integrated system is used for consequence analysis of alternate developmental scenarios, and for the choice of a preferred scenario. The case of developmental planning in the national capital region of India is presented to illustrate the effectiveness of this modeling approach.