Fine-tuning inflow prediction models integrating optimization algorithms and TRMM data for enhanced accuracy

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/).

Bibliographische Detailangaben
Veröffentlicht in:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 90(2024), 3 vom: 14. Aug., Seite 844-877
1. Verfasser: Ali, Enas (VerfasserIn)
Weitere Verfasser: Zerouali, Bilel, Tariq, Aqil, Katipoğlu, Okan Mert, Bailek, Nadjem, Santos, Celso Augusto Guimarães, M Ghoneim, Sherif S, Towfiqul Islam, Abu Reza Md
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:Journal Article data-driven frameworks discrete wavelet transform inflow prediction parameter optimization particle swarm optimization reservoir management
Beschreibung
Zusammenfassung:© 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
Beschreibung: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