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• W3136180508 abstract "Shihmen Reservoir is ranked the second largest designed storage capacity in Taiwan. The accurate forecasting of suspended sediment concentrations (SSCs) during typhoons is critical for effective reservoir management. This paper proposes a two-step switched machine learning (ML)-based approach for constructing an effective model to forecast reservoir SSCs. Different ML algorithms are adopted in the first ML step to build multiple ML-based SSC forecasting models, including multilayer perceptrons, random forest, support vector machines (SVMs), deep neural networks, recurrent neural networks, long short-term memory (LSTM) networks, and gated recurrent units. To compensate for a deficiency in measured SSC data, historical typhoons are modeled using the well-validated SRH-2D numerical model. The second step develops a switched forecasting strategy to optimally integrate forecasts from multiple ML-based models to provide more accurate calculations. The SSC forecasts obtained from the SVM and LSTM are confirmed to be superior to those from other ML-based models. The proposed model (optimally integrated from multiple ML-based models) outperforms the others, particularly when forecasting 1 and 3 h ahead. The proposed model improves the accuracy of SCC forecasts and can be used for sedimentation management in reservoirs during typhoons." @default.
• W3136180508 created "2021-03-29" @default.
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• W3136180508 date "2021-04-01" @default.
• W3136180508 modified "2023-09-23" @default.
• W3136180508 title "Real-time forecasting of suspended sediment concentrations in reservoirs by the optimal integration of multiple machine learning techniques" @default.
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• W3136180508 doi "https://doi.org/10.1016/j.ejrh.2021.100804" @default.
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