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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.

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为探索提高径流预测精度,基于兰州水文站2001年8月~2019年12月的逐日径流数据,在控制变量法的基础上,应用LSTM、ARIMA、SVR、XGBoost四种模型,建立了单一模型、EMD分解重构、剔除噪声模态分量后的EMD分解重构等三类处理方式共12种模型方案,并对12种方案的评价指标进行对比。结果表明,EMD序列分解重构技术和基于Hurst指数的噪声模态分量剔除有助于提升预测精度,与单一模型相比,前者构建的模型的均方根误差(RRMSE)平均下降了15.16%,后者平均下降了28.49%;12种方案中,预测效果较好的方案是剔除噪声模态分量后的“EMD-SVR-ARIMA”模型。

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魏霖静(1977-),女,教授,研究方向为智能计算、农业信息化,E-mail:
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路炜(1998-),女,硕士研究生,研究方向为机器学习,E-mail:

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路炜(1998-),女,硕士研究生,研究方向为机器学习,E-mail:

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基于EMD分解的黄河流域兰州水文站日径流预测
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路炜 a , 魏霖静 b
水电能源科学 | 水文水资源与环境 2023,41(8): 19-22
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水电能源科学 | 水文水资源与环境 2023, 41(8): 19-22
基于EMD分解的黄河流域兰州水文站日径流预测
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路炜a , 魏霖静b
作者信息
  • a.甘肃农业大学理学院,甘肃 兰州 730030
  • b.甘肃农业大学信息科学技术学院,甘肃 兰州 730030
  • 路炜(1998-),女,硕士研究生,研究方向为机器学习,E-mail:

通讯作者:

魏霖静(1977-),女,教授,研究方向为智能计算、农业信息化,E-mail:
Daily Runoff Prediction of Lanzhou Hydrological Station in Yellow River Basin Based on EMD Decomposition
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
出版时间: 2023-08-25 doi: 10.20040/j.cnki.1000-7709.2023.20221334
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为探索提高径流预测精度,基于兰州水文站2001年8月~2019年12月的逐日径流数据,在控制变量法的基础上,应用LSTM、ARIMA、SVR、XGBoost四种模型,建立了单一模型、EMD分解重构、剔除噪声模态分量后的EMD分解重构等三类处理方式共12种模型方案,并对12种方案的评价指标进行对比。结果表明,EMD序列分解重构技术和基于Hurst指数的噪声模态分量剔除有助于提升预测精度,与单一模型相比,前者构建的模型的均方根误差(RRMSE)平均下降了15.16%,后者平均下降了28.49%;12种方案中,预测效果较好的方案是剔除噪声模态分量后的“EMD-SVR-ARIMA”模型。

日径流预测  /  EMD分解  /  兰州水文站  /  机器学习模型

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
路炜, 魏霖静. 基于EMD分解的黄河流域兰州水文站日径流预测. 水电能源科学, 2023 , 41 (8) : 19 -22 . DOI: 10.20040/j.cnki.1000-7709.2023.20221334
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
  • 2020年甘肃农业大学研究生教育研究项目(2020-19)
  • 2021年度兰州市人才创新创业项目(2021-RC-47)
  • 2021年教育部产学研合作协同育人项目(202102326036)
2023年第41卷第8期
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doi: 10.20040/j.cnki.1000-7709.2023.20221334
  • 接收时间:2022-06-29
  • 首发时间:2026-01-28
  • 出版时间:2023-08-25
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出版历史
  • 收稿日期:2022-06-29
  • 修回日期:2022-10-19
基金
2020年甘肃农业大学研究生教育研究项目(2020-19)
2021年度兰州市人才创新创业项目(2021-RC-47)
2021年教育部产学研合作协同育人项目(202102326036)
作者信息
    a.甘肃农业大学理学院,甘肃 兰州 730030
    b.甘肃农业大学信息科学技术学院,甘肃 兰州 730030

通讯作者:

魏霖静(1977-),女,教授,研究方向为智能计算、农业信息化,E-mail:
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2种不同金属材料的力学参数

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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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