2026-10-19T01:52:44Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/111082026-10-18T21:13:16Zopenairedrivercom_ebyu_10000col_ebyu_10001

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracyAli, EnasZerouali, BilelTariq, AqilKatipog, Okan MertBailek, NadjemSantos, Celso Augusto GuimarãesGhoneim, Sherif S.M.Towfiqul Islam, Abu Reza Md.data-driven frameworksdiscrete wavelet transforminflow predictionparameter optimizationparticle swarm optimizationreservoir managementAlgorithmsMachine LearningModels, TheoreticalData accuracyDiscrete wavelet transformsPetroleum reservoir evaluationRandom forestsData drivenData-driven frameworkDiscrete-wavelet-transformInflow predictionsParticle swarmShort term memorySwarm optimizationaccuracy assessmentalgorithmbasin managementdiscrete element methodoptimizationparameterizationpredictionwavelet analysisArticleautocorrelationThis 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. © 2024 IWA Publishing. All rights reserved.IWA Publishing2024Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/11108https://doi.org/10.2166/wst.2024.2220273-1223enWater Science and Technologyinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/11108