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

Simple item page
dc.citedby33
dc.contributor.authorEhteram M.en_US
dc.contributor.authorOthman F.B.en_US
dc.contributor.authorYaseen Z.M.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorMalek M.B.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorShahid S.en_US
dc.contributor.authorSingh V.P.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid36630785100en_US
dc.contributor.authorid56436206700en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57195934440en_US
dc.contributor.authorid57211219633en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T06:51:54Z
dc.date.available2023-05-29T06:51:54Z
dc.date.issued2018
dc.descriptionDecision 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 Statesen_US
dc.description.abstractFlood 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo807
dc.identifier.doi10.3390/w10060807
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85048934380
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85048934380&doi=10.3390%2fw10060807&partnerID=40&md5=340f6c1a3796564db648a6f8df688a6e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23795
dc.identifier.volume10
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleImproving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS