Due to the complex nature of river stage-discharge process, the present study tried to develop a unique strategy to predict it precisely. The proposed conceptual strategy has some advantages to cover the shortcomings. First, it uses one model instead of several models to predict multiple points instead of one point. On the one hand, the constructed model was inspired by physical-based model (to include time-space attributes of the catchment). On the other hand, ensemble empirical mode decomposition algorithm (EEMD), wavelet transform (WT), and mutual information (MI) were employed as a hybrid pre-processing approach conjugated to support vector machine. For this end, a conceptual strategy (multi-station model) was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. First, a classic model along with WT was performed to predict the 1-day-ahead river discharge for each single station. Therefore DWT-EEMD and feature selection were used for decomposed subseries using MI to be employed in conceptual models. In the proposed feature selection method, some useless subseries were deleted to achieve better performance. The results approved efficiency of the proposed WT-EEMD-MI approach to improve accuracy of different modeling strategies.

由于河段排放过程的复杂性，本研究试图开发一种独特的策略来对其进行精确预测。提出的概念策略具有弥补缺点的一些优点。首先，它使用一个模型而不是几个模型来预测多个点而不是一个点。一方面，构建模型的灵感来自基于物理的模型（包括流域的时空属性）。另一方面，采用集成经验模式分解算法（EEMD），小波变换（WT）和互信息（MI）作为共轭支持向量机的混合预处理方法。为此，开发了一种概念性策略（多站点模型）来更准确地预测苏里斯河流量。本文使用的策略能够涵盖河流流量建模的不确定性和复杂性。首先，进行了经典模型和WT预测每个站的提前1天的河流量。因此，将DWT-EEMD和特征选择用于使用MI的分解子系列，以用于概念模型。在提出的特征选择方法中，删除了一些无用的子系列以获得更好的性能。结果证实了所提出的WT-EEMD-MI方法的效率，以提高不同建模策略的准确性。在提出的特征选择方法中，删除了一些无用的子系列以获得更好的性能。结果证实了所提出的WT-EEMD-MI方法的效率，以提高不同建模策略的准确性。在提出的特征选择方法中，删除了一些无用的子系列以获得更好的性能。结果证实了所提出的WT-EEMD-MI方法的效率，以提高不同建模策略的准确性。