Hydrological Modelling using Process based and Data Driven Models | Process-based and Neural Network Modelling in Hydrology

Ajai Singh (u. a.)

ISBN 10: 3639511077 / ISBN 13: 9783639511079

Published by Scholars' Press, 2013

About this Item

Hydrological Modelling using Process based and Data Driven Models | Process-based and Neural Network Modelling in Hydrology | Ajai Singh (u. a.) | Taschenbuch | 268 S. | Englisch | 2013 | Scholars' Press | EAN 9783639511079 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 106079304

Bibliographic Details

• Title: Hydrological Modelling using Process based and Data Driven Models | Process-based and Neural Network Modelling in Hydrology
• Publisher: Scholars' Press
• Publication Date: 2013
• Language: English
• ISBN 10: 3639511077
• ISBN 13: 9783639511079
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 417 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Point source pollution is relatively easily controllable and identifiable. Pollutants detected in a concentrated water source such as stream, river or lake are called non-point source pollution. Sedimentation and deteriorating water quality due to non-point source pollution are nowadays a very serious problem in reservoirs, lakes and rivers and is caused by high discharges of nutrients and sediment from upstream river basins. Minimization of the discharges and improvement of the agricultural practices are the obvious solution of the problem. The application of various hydrological models helps the planners and water resource managers to plan appropriately to implement effective measures. In the present study, process-based Soil and Water Assessment Tool (SWAT) and artificial neural network models such as Multi-Layer Perceptron (MLP) and Radial Basis Neural Network (RBNN) have been applied to simulate hydrological processes such as stream flow and sediment yield on monthly time steps in agricultural watershed in Eastern India. The model outputs are compared in terms of well laid out statistical criterion.

About the Author

Dr. Ajai Singh is working as Assistant Professor in Uttar Banga Krishi Viswavidyalaya, India. His recent published works are on Micro-Irrigation, Soil and Water Assessment Tool, and Neural Network modelling in simulation of hydrological processes. Prof. Mohd. Imtiyaz is presently Dean of Vaugh School of Agril. Engg. and Tech. SHIATS,Allahabad,India