The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available water and future water resources, especially for high altitude mountainous glacier melting affected basins in the climate change context. In the current study, a novel hybridized machine learning method, extended m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2022
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| Online Access: | http://eprints.utm.my/100987/1/ShamsuddinShahid2022_TheViabilityofExtendedMarinePredatorsAlgorithmBased.pdf |
| _version_ | 1796866978967715840 |
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| author | Adnan Ikram, Rana Muhammad Ewees, Ahmed A. Parmar, Kulwinder Singh Yaseen, Zaher Mundher Shahid, Shamsuddin Kisi, Ozgur |
| author_facet | Adnan Ikram, Rana Muhammad Ewees, Ahmed A. Parmar, Kulwinder Singh Yaseen, Zaher Mundher Shahid, Shamsuddin Kisi, Ozgur |
| author_sort | Adnan Ikram, Rana Muhammad |
| collection | ePrints |
| description | Precise streamflow prediction is necessary for better planning and managing available water and future water resources, especially for high altitude mountainous glacier melting affected basins in the climate change context. In the current study, a novel hybridized machine learning method, extended marine predators algorithm (EMPA)-based ANN (ANN-EMPA), is developed for streamflow estimation in the Upper Indus Basin, a key mountainous glacier melt affected basin of Pakistan. The prediction accuracy of the novel metaheuristic algorithm (EMPA) was also compared with several benchmark metaheuristic algorithms, including the marine predators algorithm (MPA), particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). The results revealed that the newly developed hybridized ANN-EMPA outperformed the other hybrid ANN methods in streamflow prediction. ANN-EMPA improved the root mean square error, mean absolute error and Nash–Sutcliffe efficiency of ANN-PSO by 4.8, 4.1 and 0.5%, ANN-GA by 6.2, 5.6 and 0.6%, ANN-GWO by 3.7, 4.4 and 0.5%, and ANN-MPA by 3.2, 7.5 and 0.3%, respectively. Month number (MN) was also examined as input to the best models to assess its impact on the prediction precision. Obtained results showed that MN generally slightly improved the models’ accuracy. Results also showed that temperature-based inputs provided better prediction accuracy than only streamflow as inputs. Therefore, the ANN-EMPA model can be used for streamflow estimation from temperature data only when long-term streamflow data is unavailable. |
| first_indexed | 2024-03-05T21:20:17Z |
| format | Article |
| id | utm.eprints-100987 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:20:17Z |
| publishDate | 2022 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-1009872023-05-23T10:25:01Z http://eprints.utm.my/100987/ The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction Adnan Ikram, Rana Muhammad Ewees, Ahmed A. Parmar, Kulwinder Singh Yaseen, Zaher Mundher Shahid, Shamsuddin Kisi, Ozgur TA Engineering (General). Civil engineering (General) Precise streamflow prediction is necessary for better planning and managing available water and future water resources, especially for high altitude mountainous glacier melting affected basins in the climate change context. In the current study, a novel hybridized machine learning method, extended marine predators algorithm (EMPA)-based ANN (ANN-EMPA), is developed for streamflow estimation in the Upper Indus Basin, a key mountainous glacier melt affected basin of Pakistan. The prediction accuracy of the novel metaheuristic algorithm (EMPA) was also compared with several benchmark metaheuristic algorithms, including the marine predators algorithm (MPA), particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). The results revealed that the newly developed hybridized ANN-EMPA outperformed the other hybrid ANN methods in streamflow prediction. ANN-EMPA improved the root mean square error, mean absolute error and Nash–Sutcliffe efficiency of ANN-PSO by 4.8, 4.1 and 0.5%, ANN-GA by 6.2, 5.6 and 0.6%, ANN-GWO by 3.7, 4.4 and 0.5%, and ANN-MPA by 3.2, 7.5 and 0.3%, respectively. Month number (MN) was also examined as input to the best models to assess its impact on the prediction precision. Obtained results showed that MN generally slightly improved the models’ accuracy. Results also showed that temperature-based inputs provided better prediction accuracy than only streamflow as inputs. Therefore, the ANN-EMPA model can be used for streamflow estimation from temperature data only when long-term streamflow data is unavailable. Elsevier Ltd 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/100987/1/ShamsuddinShahid2022_TheViabilityofExtendedMarinePredatorsAlgorithmBased.pdf Adnan Ikram, Rana Muhammad and Ewees, Ahmed A. and Parmar, Kulwinder Singh and Yaseen, Zaher Mundher and Shahid, Shamsuddin and Kisi, Ozgur (2022) The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Applied Soft Computing, 131 (NA). pp. 1-17. ISSN 1568-4946 http://dx.doi.org/10.1016/j.asoc.2022.109739 DOI : 10.1016/j.asoc.2022.109739 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Adnan Ikram, Rana Muhammad Ewees, Ahmed A. Parmar, Kulwinder Singh Yaseen, Zaher Mundher Shahid, Shamsuddin Kisi, Ozgur The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title | The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title_full | The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title_fullStr | The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title_full_unstemmed | The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title_short | The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction |
| title_sort | viability of extended marine predators algorithm based artificial neural networks for streamflow prediction |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/100987/1/ShamsuddinShahid2022_TheViabilityofExtendedMarinePredatorsAlgorithmBased.pdf |
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