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首页> 外文期刊>Journal of Hydrology >Comparison of eight filter-based feature selection methods for monthly streamflow forecasting - Three case studies on CAMELS data sets
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Comparison of eight filter-based feature selection methods for monthly streamflow forecasting - Three case studies on CAMELS data sets

机译：每月流流预测的八个基于滤波器的特征选择方法的比较 - 骆驼数据集的三种案例研究

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Recently, there has been an increased emphasis on employing data-driven models to forecast streamflow. However, in these data-driven models used for forecasting monthly streamflow, the performances of filter-based feature selection (FFS) methods have not been studied in detail. In this study, we investigated the effectiveness of eight common FFS methods, namely, linear Pearson correlation, partial linear Pearson correlation (PCI), mutual information (MI), conditional MI, partial MI, maximal relevance minimal redundancy Pearson correlation, maximal relevance minimal redundancy MI and gamma WA methods, on three regression models, namely multiple linear regression (MLR), ensemble extreme learning machine (enELM) and k-nearest neighbor (KNN) regression, for real-world one-month-ahead streamflow forecasting. The study was conducted on three cases from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) data sets. Furthermore, two termination criterion (TC) methods, the Hampel WA and resampling, were comparatively analyzed. The results of this study highlight three important findings. First, there was no dominant FFS method that coupled with enELM or KNN. Second, when resampling was applied to select a final model in the candidate combinations of the eight FFS methods and three regression models, PCI was the most favorable FFS method for the final model. Finally, the Hampel test TC was superior to the resampling TC in terms of stability and anti-overfitting. These findings have significant practical reference value for real-world monthly streamflow forecasting.
机译：最近，在采用数据驱动模型之前，增加了预测流流程。然而，在用于预测月度流流的这些数据驱动模型中，尚未详细研究基于滤波器的特征选择（FFS）方法的性能。在这项研究中，我们研究了八个常见的FFS方法的有效性，即线性Pearson相关性，部分线性Pearson相关性（PCI），互信息（MI），条件MI，部分MI，最大相关性最小冗余Pearson相关性，最大相关性最小冗余MI和伽玛WA方法，在三个回归模型，即多元线性回归（MLR），集合极限学习机（ENELM）和K最近邻（KNN）回归，用于现实世界的一个月前的流流预测。该研究在三种情况下从集水区属性和气象进行了大型样本研究（骆驼）数据集。此外，相对分析了两个终止标准（TC）方法，汉普尔瓦和重采样。本研究的结果突出了三个重要发现。首先，没有主导的FFS方法，与ENELM或KNN联系。其次，当应用重新采样时，在八个FFS方法和三个回归模型的候选组合中选择最终模型，PCI是最终模型的最有利的FFS方法。最后，在稳定性和抗过度拟合方面，汉普尔测试TC优于重采样TC。这些发现对现实世界每月流流程预测具有显着的实际参考价值。

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