For regulating urban growth, it is imperative to produce urban growth zonation maps, in which future urbanizable areas along with their urban growth potential are delineated. As, these maps provide a rational and scientific basis for taking future decisions regarding the growth of the city. The conventional approach for generating urban growth zonation maps is subjective in nature. To reduce this subjectivity, an artificial neural network (ANN) approach has been proposed for generating urban growth zonation maps. The database required for ANN-based urban growth zonation has been compiled from remote sensing data and other existing maps. GIS is used for handling of this spatial data. A comparison of the ANN- and conventional approach-derived zonation maps was also done. The study demonstrated the potential of ANN for urban growth zonation of an area, which may provide a valuable input to the urban planning authorities for regulating urban growth
No takes yet. Share an insight, caveat, or question.
Maithani et al. (2010) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: