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Daily Runoff Prediction of Lanzhou Hydrological Station in Yellow River Basin Based on EMD Decomposition
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Wei LUa, Lin-jing WEIb
Water Resources and Power | 2023, 41(8) : 19 - 22
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Water Resources and Power | 2023, 41(8): 19-22
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Daily Runoff Prediction of Lanzhou Hydrological Station in Yellow River Basin Based on EMD Decomposition
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Wei LUa, Lin-jing WEIb
Affiliations
  • a.College of Science, Gansu Agricultural University, Lanzhou 730030, China
  • b.College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730030, China
Published: 2023-08-25 doi: 10.20040/j.cnki.1000-7709.2023.20221334
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In order to improve the accuracy of runoff prediction, based on the control variable method and the daily runoff data of Lanzhou hydrometric station from August 2001 to December 2019, the models of the LSTM, ARIMA, SVR and XGBoost were used to establish 12 model schemes, including single model, EMD decomposition and reconstruction, EMD decomposition and reconstruction after removing noise components, and evaluation indicators of the 12 schemes were compared. The results show that the EMD sequence decomposition and reconstruction technology and noise component elimination based on Hurst exponent are helpful to improve the prediction accuracy. Compared with the single model, the RRMSE of the model constructed by the former decreased by 15.16% on average, and that of the latter decreased by 28.49% on average. Among the 12 schemes, EMD-SVR-ARIMA with noise components removed is the best model.

daily runoff forecasting  /  EMD decomposition  /  Lanzhou hydrological station  /  machine learning model
Wei LU, Lin-jing WEI. Daily Runoff Prediction of Lanzhou Hydrological Station in Yellow River Basin Based on EMD Decomposition[J]. Water Resources and Power, 2023 , 41 (8) : 19 -22 . DOI: 10.20040/j.cnki.1000-7709.2023.20221334
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221334
  • Receive Date：2022-06-29
  • Online Date：2026-01-28
  • Published：2023-08-25
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  • Received：2022-06-29
  • Revised：2022-10-19
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    a.College of Science, Gansu Agricultural University, Lanzhou 730030, China
    b.College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730030, China
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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
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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