ABSTRACT Streamflow prediction is useful for robust water resources engineering and management. This paper introduces a new methodology to generate more effective features for streamflow prediction based on the concept of “interaction effect”. The new features (input variables) are derived from the original features in a process called feature generation. It is necessary to select the most efficient input variables for the modelling process. Two feature selection methods, least absolute shrinkage and selection operator (LASSO) and particle swarm optimization-artificial neural networks (PSO-ANN), are used to select the effective features. Principal components analysis (PCA) is used to reduce the dimensions of selected features. Then, optimized support vector regression (SVR) is used for monthly streamflow prediction at the Karaj River in Iran. The proposed method provided accurate prediction results with a root mean square error (RMSE) of 2.79 m3/s and determination coefficient (R2 ) of 0.92.

中文翻译：

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使用与智能模型集成的混合特征选择方法模拟月流量

摘要 水流预测对于稳健的水资源工程和管理很有用。本文基于“交互效应”的概念，介绍了一种新的方法，可以为流量预测生成更有效的特征。新特征（输入变量）是在称为特征生成的过程中从原始特征导出的。有必要为建模过程选择最有效的输入变量。两种特征选择方法，最小绝对收缩和选择算子（LASSO）和粒子群优化-人工神经网络（PSO-ANN），用于选择有效特征。主成分分析 (PCA) 用于减少所选特征的维度。然后，优化支持向量回归（SVR）用于伊朗卡拉季河的月流量预测。