Abstract

ABSTRACT Determination of total impervious area (TIA) or effective impervious area (EIA) is mandatory for hydrological modelling of water quantity and quality in urban areas. In this study, a multilayer deep learning model Convolutional Neural Network (CNN) is implemented for estimating TIA. A more realistic automated method is suggested to determine EIA by integrating the remote sensing data, the digital format of the drainage network, and a digital elevation model (DEM). A graphical user interface (GUI) called EIA estimator is developed for automatic creation of EIA maps. An effort is made to derive a relationship between TIA and EIA. Several power relationships are obtained for easily measurable TIA and hydraulically relevant EIA in urban catchments of India. These relationships would aid planners and decision-makers with quick initial estimates for surface water quantity and quality problems.

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