Title: Daily discharge simulation: combining semi-distributed GIS-based and artificial intelligence models

Authors: Ali H. Ahmed Suliman; Ayob Katimon; Intan Zaurah Mat Darus

Addresses: Department of Geography, College of Education, University of Al-Hamdaniya, Nineveh Plain, Iraq ' School of Bioprocess Engineering, Universiti Malaysia Perlis (UniMAP), 01000 Kangar, Perlis, Malaysia ' Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Darul Takzim, Malaysia

Abstract: Developing highly accurate semi-distributed rainfall runoff models are still a big challenge in streamflow simulation. In this paper, a new technique using ANN to improve the accuracy of TOPMODEL is presented. TOPMODEL contains three sub-models, which are root storage, gravity storage and saturated storage. The proposed scheme is to replace one of the sub-models by artificial neural networks (ANN) model. A medium catchment located in tropical Malaysia known as Rantau Panjang catchment (RPC) is used. Two years, 1998-1999, are used for calibration, and 2000-2001 are used for validation process using daily data sets. Model results are evaluated by Nash-Sutcliffe model (NS), relative volume error (RVE) and correlation coefficient (CoC) which have been improved from 0.63 to 0.86, 0.92 to 0.93 and 40.91 to 14.12 respectively demonstrate the ability of ANN to improve the accuracy of TOPMODEL. It is concluded that the scheme can improve performance in terms of streamflow simulation.

Keywords: TOPMODEL-Simulink; Johor River Basin; hybrid; artificial neural networks; ANN; MLP; artificial intelligence; tropical catchment; Rantau Panjang; rainfall runoff models; Malaysia.

DOI: 10.1504/IJHST.2020.109946

International Journal of Hydrology Science and Technology, 2020 Vol.10 No.5, pp.471 - 486

Received: 20 Aug 2018
Accepted: 21 Nov 2018

Published online: 30 Sep 2020 *

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