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Research on Sand Discharge Prediction of Wanjiazhai Reservoir Based on Machine Learning Algorithms
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Xiao-fei YAN1, 2, Xiu-ji GUO1, 2, Long-fei SUN1, 2
Water Resources and Power | 2023, 41(3) : 79 - 82
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Water Resources and Power | 2023, 41(3): 79-82
• DAM SAFETY AND MONITORING •
Research on Sand Discharge Prediction of Wanjiazhai Reservoir Based on Machine Learning Algorithms
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Xiao-fei YAN1, 2, Xiu-ji GUO1, 2, Long-fei SUN1, 2
Affiliations
  • 1.Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
  • 2.Key Laboratory of Lower Yellow River Channel and Estuary Regulation, MWR, Zhengzhou 450003, China
Published: 2023-03-25 doi: 10.20040/j.cnki.1000-7709.2023.20220677
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In order to overcome the difficult problem of establishing multi-factor and non-linear complex relationship of reservoir sand discharge and achieve its accurate prediction, four machine learning algorithms including XGBoost, KNN, SVR and RF were used to predict and analyze the sand content of reservoir outflow based on the series data of Wanjiazhai reservoir from 2002 to 2020, respectively. The results show that the use of machine learning algorithms can effectively realize the reservoir discharge prediction considering different influencing factors. The applicability of different machine learning algorithms in reservoir discharge prediction varies. In comparison, the highest coefficient of determination R2 of the reservoir discharge prediction model based on RF algorithm is 0.9349, and the corresponding average absolute error and root mean square error are the smallest, which are 2.974 and 4.886, respectively. The prediction effect of the RF algorithm is better than the other three algorithms. The proposed method can provide a theoretical basis for accurate prediction of reservoir sand discharge and optimization of scheduling scheme.

reservoir sand discharge  /  sand content  /  machine learning algorithm  /  prediction model
Xiao-fei YAN, Xiu-ji GUO, Long-fei SUN. Research on Sand Discharge Prediction of Wanjiazhai Reservoir Based on Machine Learning Algorithms[J]. Water Resources and Power, 2023 , 41 (3) : 79 -82 . DOI: 10.20040/j.cnki.1000-7709.2023.20220677
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20220677
  • Receive Date：2022-04-07
  • Online Date：2026-01-28
  • Published：2023-03-25
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History
  • Received：2022-04-07
  • Revised：2022-06-13
Funding
Affiliations
    1.Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
    2.Key Laboratory of Lower Yellow River Channel and Estuary Regulation, MWR, Zhengzhou 450003, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20220677
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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