Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm
Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy asse...
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2023
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my.uniten.dspace-237952023-05-29T14:51:54Z Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm Ehteram M. Othman F.B. Yaseen Z.M. Afan H.A. Allawi M.F. Malek M.B.A. Ahmed A.N. Shahid S. Singh V.P. El-Shafie A. 57113510800 36630785100 56436206700 56436626600 57057678400 55636320055 57214837520 57195934440 57211219633 16068189400 Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States 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. � 2018 by the authors. Final 2023-05-29T06:51:54Z 2023-05-29T06:51:54Z 2018 Article 10.3390/w10060807 2-s2.0-85048934380 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048934380&doi=10.3390%2fw10060807&partnerID=40&md5=340f6c1a3796564db648a6f8df688a6e https://irepository.uniten.edu.my/handle/123456789/23795 10 6 807 All Open Access, Gold MDPI AG Scopus |
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Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States |
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57113510800 |
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57113510800 Ehteram M. Othman F.B. Yaseen Z.M. Afan H.A. Allawi M.F. Malek M.B.A. Ahmed A.N. Shahid S. Singh V.P. El-Shafie A. |
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Article |
| author |
Ehteram M. Othman F.B. Yaseen Z.M. Afan H.A. Allawi M.F. Malek M.B.A. Ahmed A.N. Shahid S. Singh V.P. El-Shafie A. |
| spellingShingle |
Ehteram M. Othman F.B. Yaseen Z.M. Afan H.A. Allawi M.F. Malek M.B.A. Ahmed A.N. Shahid S. Singh V.P. El-Shafie A. Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| author_sort |
Ehteram M. |
| title |
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| title_short |
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| title_full |
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| title_fullStr |
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| title_full_unstemmed |
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| title_sort |
improving the muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806424274952519680 |
| score |
13.214268 |