Next Article in Journal
Research on Cascade Reservoirs’ Short-Term Optimal Operation under the Effect of Reverse Regulation
Next Article in Special Issue
Flood Hydrograph Prediction Using Machine Learning Methods
Previous Article in Journal
Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network
Previous Article in Special Issue
Physical Hybrid Neural Network Model to Forecast Typhoon Floods
Open AccessArticle

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Department of Civil Engineering, College of Engineering, University Tenaga National, Kajang 43000, Malaysia
Institute of Policy Energy Research (IPERe), Universiti Tenaga National, Kajang 43000, Malaysia
Institute for Energy Infrastructures, University Tenaga National, Kajang 43000, Malaysia
Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, 2117 TAMU, College Station, TX 77843-2117, USA
Author to whom correspondence should be addressed.
Water 2018, 10(6), 807;
Received: 21 May 2018 / Revised: 12 June 2018 / Accepted: 15 June 2018 / Published: 19 June 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time. View Full-Text
Keywords: bat algorithm; particle swarm optimization; flood routing; Muskingum model bat algorithm; particle swarm optimization; flood routing; Muskingum model
Show Figures

Figure 1

MDPI and ACS Style

Ehteram, M.; Binti Othman, F.; Mundher Yaseen, Z.; Abdulmohsin Afan, H.; Falah Allawi, M.; Bt. Abdul Malek, M.; Najah Ahmed, A.; Shahid, S.; P. Singh, V.; El-Shafie, A. Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm. Water 2018, 10, 807.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop