长江上游水库入库流量的中长期预报
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1.南京水利科学研究院;2.河海大学

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国家重点基础研究发展计划(973计划),国家自然科学基金项目(面上项目,重点项目,重大项目)


Medium and Long-term Forecast of the Reservoir Inflow in the Upper Yangtze River
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Affiliation:

1.Nanjing Hydraulic Research Institute;2.Hohai Universiy

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    摘要:

    为了对比数理统计模型与机器学习模型在中长期径流预报中的特点与适用性,挑选逐步回归与随机森林两种方法构建中长期预报模型。以气象因子的物理机制为基础,结合了单相关系数及随机森林重要性分析识别关键气象因子,作为模型输入。训练了长江上游乌东德、瀑布沟两个水库1959—1998年的入库径流量,且预测了两个水库1999—2014年的入库径流量。结果显示,两种模型的训练效果良好,稳定性强,随机森林的预测结果比逐步回归的精度高,但精度的差距较小;随机森林能减少预测因子值的异常变化带来的拟合误差,但过拟合问题更为明显;研究方法对制定长江上游流域调度方案具有重要的应用价值,研究结果对理解数理统计与机器学习两类模型在中长期预报应用中的特点具有参考价值。

    Abstract:

    In order to compare the characteristics and applicability of the mathematical statistical model and the machine learning model in medium and long-term runoff forecasting, stepwise regression and random forest are selected to build medium and long-term forecasting model. Based on the physical mechanism of meteorological factors, single correlation coefficient and random forest importance analysis are combined to select key meteorological factors as input to the model. The runoff flows from the two reservoirs of Wudongde and Pubugou in the upper Yangtze River from 1959 to 1998 were simulated, and the runoff flows of the two reservoirs from 1999 to 2014 were predicted. The results show that the overall simulation effect of the two models is good and the stability is strong. The accuracy of the prediction results of the random forest is higher than that of the stepwise regression, but the difference in accuracy is small. Random forest can reduce the fitting error caused by the abnormal change of the predictor value, but the overfitting problem is more obvious. The research method has important application value for the formulation of the upper Yangtze River Basin operation scheme, and the research results have reference value for understanding the characteristics of mathematical statistics and machine learning models in the application of mid-long term forecasting.

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张轩,张行南,王高旭,等.长江上游水库入库流量的中长期预报[J].水资源保护,,():131-136

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  • 收稿日期:2021-01-19
  • 最后修改日期:2022-07-11
  • 录用日期:2021-05-06
  • 在线发布日期: 2022-06-28
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