An automated approach to establish relationship between total and effective impervious area for urban Indian catchments

Abstract

ABSTRACTDetermination 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.KEYWORDS: TIAEIARSGISurbanimperviousness Disclosure statementThe authors declare that they don’t have any conflict of interest.Availability of data and materialPart of data may be available on request.Additional informationFundingThe authors would like to acknowledge the Science and Engineering Research Board (SERB), India for providing the financial support for this research program, project no. [ECR/2016/000057].