堆叠集成模型径流预报效果的影响因素研究
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中山大学地理科学与规划学院

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TP3

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Study on Influence Factors about Runoff Forecasting Performance of Stacking Integrated Model
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School of geography and planning,Sun Yat-sen University,Guangzhou

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    为了研究堆叠集成模型预报效果的可能影响因素,本文以安墩水流域为例,选择支持向量回归、多元线性回归、长短期记忆神经网络、前馈神经网络、梯度提升回归树、自回归积分滑动平均模型以及自适应增强算法作为基学习器,选择多元线性回归、支持向量回归、多层感知机作为元学习器,建立多个堆叠集成模型,并以平均绝对误差、相关系数、均方根误差、平均相对误差、达标率、纳什效率系数等评价指标对各集成模型的预报效果进行了对比。研究表明,堆叠集成模型的预测效果与基学习器的数量无关,与基学习器的质量呈正相关关系。此外,不同的元学习器选择也会对堆叠集成模型的预测效果产生影响。该研究可为利用堆叠集成模型进行径流预报提供科学指导。

    Abstract:

    In order to study the possible influencing factors of the forecasting effect of stacking integrated model, this paper takes Andun River Basin as an example, selects support vector regression, multiple linear regression, long short-term memory neural network, feedforward neural network, gradient boosting regression tree, autoregressive integral moving average model and adaptive boosting algorithm as the base learners, and selects multiple linear regression, support vector regression and multilayer perceptron as meta learners to establish stacking ensemble models. The prediction effects of each ensemble model are compared by the average absolute error, correlation coefficient, root mean square error, average relative error, quality rate, Nash efficiency coefficient. The results show that the prediction effect of stacking ensemble model is not related to the number of base-learners, but positively related to the quality of base-learners. In addition, different selection of meta-learners will also affect the prediction effect of stacking ensemble model. This study can provide scientific guidance for runoff forecasting using stacking integrated model.

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  • 收稿日期:2021-09-28
  • 最后修改日期:2021-09-28
  • 录用日期:2022-02-17
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