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

Mohammad Ehteram, Faridah Binti Othman, Zaher Mundher Yaseen, Haitham Abdulmohsin Afan, Mohammed Falah Allawi, Marlinda Bt Abdul Malek, Ali Najah Ahmed, Shamsuddin Shahid, Vijay P. Singh, Ahmed El-Shafie

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number807
JournalWater (Switzerland)
Volume10
Issue number6
DOIs
Publication statusPublished - 19 Jun 2018

Fingerprint

flood routing
swarms
bat
Particle swarm optimization (PSO)
Chiroptera
natural disaster
methodology
peak discharge
Water Resources
prediction
disasters
Disasters
disaster
Water resources
water resources
water resource
method
particle
decision maker
case studies

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Ehteram, Mohammad ; Othman, Faridah Binti ; Yaseen, Zaher Mundher ; Afan, Haitham Abdulmohsin ; Allawi, Mohammed Falah ; Malek, Marlinda Bt Abdul ; Ahmed, Ali Najah ; Shahid, Shamsuddin ; Singh, Vijay P. ; El-Shafie, Ahmed. / Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm. In: Water (Switzerland). 2018 ; Vol. 10, No. 6.
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abstract = "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.",
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Ehteram, M, Othman, FB, Yaseen, ZM, Afan, HA, Allawi, MF, Malek, MBA, Ahmed, AN, Shahid, S, Singh, VP & El-Shafie, A 2018, 'Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm', Water (Switzerland), vol. 10, no. 6, 807. https://doi.org/10.3390/w10060807

Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm. / Ehteram, Mohammad; Othman, Faridah Binti; Yaseen, Zaher Mundher; Afan, Haitham Abdulmohsin; Allawi, Mohammed Falah; Malek, Marlinda Bt Abdul; Ahmed, Ali Najah; Shahid, Shamsuddin; Singh, Vijay P.; El-Shafie, Ahmed.

In: Water (Switzerland), Vol. 10, No. 6, 807, 19.06.2018.

Research output: Contribution to journalArticle

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AU - Othman, Faridah Binti

AU - Yaseen, Zaher Mundher

AU - Afan, Haitham Abdulmohsin

AU - Allawi, Mohammed Falah

AU - Malek, Marlinda Bt Abdul

AU - Ahmed, Ali Najah

AU - Shahid, Shamsuddin

AU - Singh, Vijay P.

AU - El-Shafie, Ahmed

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