Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) wit...
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| Format: | Article |
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Elsevier B.V.
2021
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| _version_ | 1796865765439176704 |
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| author | Adnan, Rana Muhammad R. Mostafa, Reham Kisi, Ozgur Yaseen, Zaher Mundher Shahid, Shamsuddin Zounemat-Kermani, Mohammad |
| author_facet | Adnan, Rana Muhammad R. Mostafa, Reham Kisi, Ozgur Yaseen, Zaher Mundher Shahid, Shamsuddin Zounemat-Kermani, Mohammad |
| author_sort | Adnan, Rana Muhammad |
| collection | ePrints |
| description | Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction. |
| first_indexed | 2024-03-05T21:02:03Z |
| format | Article |
| id | utm.eprints-94150 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:02:03Z |
| publishDate | 2021 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-941502022-02-28T13:32:43Z http://eprints.utm.my/94150/ Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization Adnan, Rana Muhammad R. Mostafa, Reham Kisi, Ozgur Yaseen, Zaher Mundher Shahid, Shamsuddin Zounemat-Kermani, Mohammad TA Engineering (General). Civil engineering (General) Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction. Elsevier B.V. 2021-10-27 Article PeerReviewed Adnan, Rana Muhammad and R. Mostafa, Reham and Kisi, Ozgur and Yaseen, Zaher Mundher and Shahid, Shamsuddin and Zounemat-Kermani, Mohammad (2021) Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-Based Systems, 230 . ISSN 0950-7051 http://dx.doi.org/10.1016/j.knosys.2021.107379 DOI:10.1016/j.knosys.2021.107379 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Adnan, Rana Muhammad R. Mostafa, Reham Kisi, Ozgur Yaseen, Zaher Mundher Shahid, Shamsuddin Zounemat-Kermani, Mohammad Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title | Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title_full | Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title_fullStr | Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title_full_unstemmed | Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title_short | Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| title_sort | improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization |
| topic | TA Engineering (General). Civil engineering (General) |
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