Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm

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...

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Main Authors: 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.
Format: Article
Language:English
Published: MDPI AG 2018
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Online Access:http://eprints.utm.my/id/eprint/79714/1/ShamsuddinShahid2018_ImprovingtheMuskingumFloodRouting.pdf
http://eprints.utm.my/id/eprint/79714/
http://dx.doi.org/10.3390/w10060807
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spelling my.utm.797142019-01-28T06:37:44Z http://eprints.utm.my/id/eprint/79714/ 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. TA Engineering (General). Civil engineering (General) 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. MDPI AG 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79714/1/ShamsuddinShahid2018_ImprovingtheMuskingumFloodRouting.pdf Ehteram, M. and Othman, F. B. and Yaseen, Z. M. and Afan, H. A. and Allawi, M. F. and Malek, M. B. A. and Ahmed, A. N. and Shahid, S. and Singh, V. P. and El-Shafie, A. (2018) Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm. Water (Switzerland), 10 (6). ISSN 2073-4441 http://dx.doi.org/10.3390/w10060807 DOI:10.3390/w10060807
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
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
description 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.
format 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.
author_facet 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.
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 2018
url http://eprints.utm.my/id/eprint/79714/1/ShamsuddinShahid2018_ImprovingtheMuskingumFloodRouting.pdf
http://eprints.utm.my/id/eprint/79714/
http://dx.doi.org/10.3390/w10060807
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score 13.222552