Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Issue 11 (17th August 2016)

• Record Type: Journal Article
• Authors: Khan, M.Y.A.; Hasan, F.; Panwar, S.; Chakrapani, G.J.
• Is Part Of: Hydrological sciences journal. Volume 61:Issue 11(2016)
• Page Start: 2084
• Page End: 2095
• Publication Date: 2016-08-17
• Subjects: Ramganga River -- Ganga Basin -- ANN -- discharge -- water level; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2015.1083650
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: Discharges and water levels are essential components of river hydrodynamics. In unreachable terrains and ungauged locations, it is quite difficult to measure these parameters due to rugged topography. In the present study an artificial neural network model has been developed for the Ramganga River catchment of the Ganga Basin. The modelled network is trained, validated and tested using daily water flow and level data pertaining to 4 years (2010–2013). The network has been optimized using an enumeration technique and a network topology of 4-10-2 with a learning rate set at 0.06, which was found optimum for predicting discharge and water-level values for the considered river. The mean square error values obtained for discharge and water level for the tested data were found to be 0.046 and 0.012, respectively. Thus, monsoon flow patterns can be estimated with an accuracy of about 93.42%.Editor M.C. Acreman;Associate editor E. Gargouri