Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.
Water Sci Technol
; 90(3): 844-877, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-39141038
ABSTRACT
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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MEDLINE
Assunto principal:
Algoritmos
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Aprendizado de Máquina
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Modelos Teóricos
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article