Quality and quantity of streamflow are crucial components in the management and control of water resources according which are challenging due to their nonstationarity and uncertainty path. This paper presented an ensemble data pre-processing-based machine learning (ML) algorithm for the decision-support of water resource management and water pollution control at the watershed scale due to the nonlinear path of streamflow. In the proposed hybrid model, a new time–frequency analysis algorithm, variational mode decomposition (VMD), is implemented to deal with the nonlinearity and nonstationary of a streamflow process. The VMD is exploited to decompose the original water quality and quantity series into a series of intrinsic mode functions (IMFs) with different frequencies. Therefore, an ensemble algorithm, bootstrap aggregating (bagging) algorithm is coupled with two common ML, reduced error pruning tree (REPT) and random forest (RF), to predict all the decomposed modes using VMD. Then, in order to reduce the variance among the base classifiers of the proposed ML, a bootstrap aggregation technique was recruited. Finally, the predicting value of the original water quality and quantity series is obtained by adding up the predicting results of all the decomposed modes. The proposed hybrid decomposition–ensemble model has been applied to two stations in Karoon River, Iran. Results obtained from this study indicate that the proposed hybrid decomposition–ensemble model can capture the nonlinear characteristics of a streamflow process in terms of water quality and quantity simultaneously and thus provide more accurate predicting results compared with those models without data frequency decomposing.

流量的质量和数量是水资源管理和控制的关键组成部分，由于其非平稳性和不确定性，因此具有挑战性。本文提出了一种基于集成数据预处理的机器学习 (ML) 算法，用于支持流域尺度的水资源管理和水污染控制的决策支持，因为水流的路径是非线性的。在所提出的混合模型中，实现了一种新的时频分析算法，变分模态分解 (VMD)，以处理水流过程的非线性和非平稳性。利用 VMD 将原始水质和水量序列分解为一系列具有不同频率的固有模态函数 (IMF)。因此，一个集成算法，bootstrap 聚合（bagging）算法与两种常见的 ML 相结合，减少错误修剪树（REPT）和随机森林（RF），使用 VMD 预测所有分解模式。然后，为了减少所提出的 ML 的基分类器之间的差异，采用了自举聚合技术。最后，将所有分解模式的预测结果相加，得到原始水质水量序列的预测值。所提出的混合分解-集合模型已应用于伊朗卡隆河的两个站点。