Ehteram, Mohammad and Othman, Faridah and Yaseen, Zaher Mundher and Afan, Haitham Abdulmohsin and Allawi, Mohammed Falah and Malek, Marlinda Abdul and Ahmed, Ali Najah and Shahid, Shamsuddin and Singh, Vijay P. and El-Shafie, Ahmed (2018) Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm. Water, 10 (6). p. 807. ISSN 2073-4441, DOI https://doi.org/10.3390/w10060807.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | bat algorithm; particle swarm optimization; flood routing; Muskingum model |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mr Faizal 2 |
| Date Deposited: | 28 Jan 2015 03:33 |
| Last Modified: | 29 Aug 2019 08:51 |
| URI: | http://eprints.um.edu.my/id/eprint/12442 |
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