Fine-tuning inflow prediction models : integrating optimization algorithms and TRMM data for enhanced accuracy
© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
| Publié dans: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 90(2024), 3 vom: 14. Aug., Seite 844-877 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Sujets: | Journal Article data-driven frameworks discrete wavelet transform inflow prediction parameter optimization particle swarm optimization reservoir management |
| Résumé: | © 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns |
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| Description: | Date Completed 14.08.2024 Date Revised 14.08.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2024.222 |