Real-time forecasting of suspended sediment concentrations reservoirs by the optimal integration of multiple machine learning techniques

Journal of Hydrology: Regional Studies

Ming-Chang Wu¹  Cheng-Chia Huang²  Gwo-Fong Lin³  Ming-Jui Chang³  Po-Hsiang Wang⁴ 

[1] Corresponding author at: Center for General Education, National Taipei University of Business, Taipei, 10051, Taiwan.;Center for General Education, National Taipei University of Business, Taipei, 10051, Taiwan;Department of Civil Engineering, National Taiwan University, Taipei, 10617, Taiwan;National Applied Research Laboratories, Taipei, 10622, Taiwan;

关键词: Reservoir operation;    Suspended sediment concentration;    SRH-2D;    Machine learning;    Switched forecasting;    Optimal integration;   

DOI  :  

来源: DOAJ

【 摘 要 】

Study region: Shihmen Reservoir is ranked the second largest designed storage capacity in Taiwan. Study focus: 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. New hydrological insights: 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.

【 授权许可】

Unknown