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Open AccessArticle

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

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Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
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Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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Department of Civil Engineering, College of Engineering, University Tenaga National, Kajang 43000, Malaysia
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Institute of Policy Energy Research (IPERe), Universiti Tenaga National, Kajang 43000, Malaysia
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Institute for Energy Infrastructures, University Tenaga National, Kajang 43000, Malaysia
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Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, 2117 TAMU, College Station, TX 77843-2117, USA
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Author to whom correspondence should be addressed.
Water 2018, 10(6), 807; https://doi.org/10.3390/w10060807
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
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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.

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