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Journal of the Indian Society of Remote Sensing

, Volume 37, Issue 3, pp 363–376 | Cite as

A neural network based urban growth model of an Indian city

  • S. MaithaniEmail author
Research Article

Abstract

The aim of the study reported in this paper is to demonstrate that the subjectivity in urban growth modeling and the calibration time can be reduced by using objective techniques like Artificial neural network (ANN). As a case study, the ANN-based model was applied to simulate the urban growth of Saharanpur city in India. In the proposed model, remote sensing and GIS were used to generate site attributes, while ANN was used to reveal the relationships between urban growth potential and the site attributes. Once ANN learnt the relationship, it was then used to simulate the urban growth. Different feed forward ANN architectures were evaluated in this study and finally the most optimum ANN architecture was selected for future growth simulation.

The simulated urban growth maps were evaluated on a cell by cell matching using Kappa index and three spatial metrices namely, Mean Patch Fractal Dimension, Landscape Shape Index and Percentage of like Adjacencies. The most optimal architecture was then used subsequently for simulating the future urban growth. The study results thus, demonstrated that the ANN-based model can objectively simulate urban growth, besides successfully coupling GIS, remote sensing and ANN.

Keywords

Spatial urban growth Artificial neural network Multilayer perceptron Spatial metrics Kappa Urban growth potential 

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Copyright information

© Indian Society of Remote Sensing 2009

Authors and Affiliations

  1. 1.Human Settlement Analysis DivisionIndian Institute of Remote SensingDehradunIndia

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