Accurate and reliable predictors selection and model construction are the key to medium and long-term runoff forecast. In this study, 130 climate indexes are utilized as the primary forecast factors. Partial Mutual Information (PMI), Recursive Feature Elimination (RFE) and Classification and Regression Tree (CART) are respectively employed as the typical algorithms of Filter, Wrapper and Embedded based on Feature Selection (FS) to obtain three final forecast schemes. Random Forest (RF) and Extreme Gradient Boosting (XGB) are respectively constructed as the representative models of Bagging and Boosting based on Ensemble Learning (EL) to realize the forecast of the three types of forecast lead time which contains monthly, seasonal and annual runoff sequences of the Three Gorges Reservoir in the Yangtze River Basin. This study aims to summarize and compare the applicability and accuracy of different FS methods and EL models in medium and long-term runoff forecast. The results show the following: (1) RFE method shows the best forecast performance in all different models and different forecast lead time. (2) RF and XGB models are suitable for medium and long-term runoff forecast but XGB presents the better forecast skills both in calibration and validation. (3) With the increase of the runoff magnitudes, the accuracy and reliability of forecast are improved. However, it is still difficult to establish accurate and reliable forecasts only large-scale climate indexes used. We conclude that the theoretical framework based on Machine Learning could be useful to water managers who focus on medium and long-term runoff forecast.

中文翻译：

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基于大规模气象数据和机器学习算法的中长期径流预报方法

准确可靠的预测因子选择和模型构建是中长期径流预测的关键。在这项研究中，将130个气候指数用作主要的预测因子。基于特征选择（FS）分别使用部分互信息（PMI），递归特征消除（RFE）和分类回归树（CART）作为典型的过滤器，包装器和嵌入式算法，以获得三个最终的预测方案。基于集成学习（EL），分别构建了随机森林（RF）和极端梯度增强（XGB）作为装袋和增强的代表模型，以实现对预测提前时间的三种类型的预测，包括月，季节和年度径流长江流域三峡水库的层序 本研究旨在总结和比较不同的FS方法和EL模型在中长期径流预报中的适用性和准确性。结果表明：（1）RFE方法在所有不同模型和不同预测交货时间下均表现出最佳的预测性能。（2）RF和XGB模型适用于中长期径流预报，但XGB在校准和验证方面显示出更好的预报技能。（3）随着径流大小的增加，预报的准确性和可靠性得到提高。但是，仅使用大规模的气候指数仍难以建立准确可靠的预报。我们得出结论，基于机器学习的理论框架对于专注于中长期径流预报的水利管理者可能有用。