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首页> 外文期刊>Water Resources Management >Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting
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Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

机译：二进制灰羽狼优化 - 正规化的极端学习机包装器与每月流流预测的Boruta算法相结合

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摘要

Input variable selection plays a key role in data-driven streamflow forecasting models. In this study, we propose a two-stage wrapper model to drive one-month-ahead streamflow forecasting in the context of high-dimensional candidate input variables. Initially, the Boruta algorithm, a feature selection method, was applied to select all the relevant input variables for the streamflow series. Then, a novel binary grey wolf optimizer (BGWO)-regularized extreme learning machine (RELM) wrapper was derived. We carried out experiments on two US catchments with 132 candidate input variables, including local meteorological information, global climatic indices, and lags of the streamflow series. Furthermore, the sensitivities of the proposed model in terms of the optimal objective function were compared. The results indicate two important findings. First, the proposed model outperformed commonly used models in terms of four error evaluation criteria. Second, for the proposed model, the root mean square error is a more suitable criterion than the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for the optimal objective function. These findings are of great reference value for developing ELM models for streamflow forecasting.
机译：输入变量选择在数据驱动的流流预测模型中播放关键作用。在这项研究中，我们提出了一个两阶段包装模型，以在高维候选输入变量的上下文中驱动一个月前的流流预测。最初，Boruta算法（一个特征选择方法）被应用于为流汇流系列选择所有相关输入变量。然后，推导出一种新型二进制灰狼优化器（BGWO）-Regularized的极限学习机（Relm）包装器。我们对两个美国集水区进行了实验，具有132名候选输入变量，包括当地气象信息，全球气候指数和流流量系列的滞后。此外，比较了所提出的模型对最佳目标函数的敏感性。结果表明了两个重要的发现。首先，在四个错误评估标准方面，所提出的模型优于常用的模型。其次，对于所提出的模型，根均方误差是比Akaike信息标准（AIC）和贝叶斯信息标准（BIC）更合适的标准，用于最佳目标函数。这些发现对于开发用于流流预测的ELM模型具有很大的参考价值。

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