期刊
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
卷 15, 期 1, 页码 1811-1829出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19942060.2021.1990133
关键词
Suspended sediment load; Machine learning; Artificial intelligence; intrinsic time-scale decomposition technique; evolutionary polynomial regression
资金
- Scientific Research Fund of Zhejiang Provincial Education Department+Research on Key Technologies for Intelligent Prediction of Soil Erosion [Y202147738]
- TaifUniversity, Taif, Saudi Arabia [TURSP-2020/114]
- Publication Fund of the TU Dresden
Estimation of suspended sediment load (SSL) is crucial for water resources management, with the ITD-EPR method proving to be the most accurate in predicting SSL at the Sarighamish and Varand Stations in Iran. This method outperformed other approaches like MT and SRC, showcasing its superior predictive capabilities.
Suspended sediment load (SSL) estimation is essential for both short- and long-term water resources management. Suspended sediments are taken into account as an important factor of the service life of hydraulic structures such as dams. The aim of this research is to estimat SSL by coupling intrinsic time-scale decomposition (ITD) and two kinds of DDM, namely evolutionary polynomial regression (EPR) and model tree (MT) DDMs, at the Sarighamish and Varand Stations in Iran. Measured data based on their lag times are decomposed into several proper rotation components (PRCs) and a residual, which are then considered as inputs for the proposed model. Results indicate that the prediction accuracy of ITD-EPR is the best for both the Sarighamish (R (2 )= 0.92 and WI = 0.96) and Varand (R (2 )= 0.92 and WI = 0.93) Stations (WI is the Willmott index of agreement), while a standalone MT model performs poorly for these stations compared with other approaches (EPR, ITD-EPR and ITD-MT) although peak SSL values are approximately equal to those by ITD-EPR. Results of the proposed models are also compared with those of the sediment rating curve (SRC) method. The ITD-EPR predictions are remarkably superior to those by the SRC method with respect to several conventional performance evaluation metrics.
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