Abstract Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing to the high number of interrelated hydrological processes. It is well-known that machine learning models could fail in simulating streamflows from only meteorological variables in the absence of antecedent streamflow values. The main reason for this could be low and lagged relationships between streamflow and meteorological variables. To overcome this inefficiency, for the first time, we developed a simulation framework by coupling a base flow separation method to three machine learning methods. It was demonstrated that separating streamflow into different components such as base flow and surface flow can be useful for improving simulation and forecasting capabilities of machine learning models. We simulated streamflow in four rivers in the United States with Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Random Forest (RF) as a function of precipitation (P), temperature (T) and potential evapotranspiration (PET). We concluded that the base flow separation method improved the simulation performances of the machine learning models to a certain degree. Apart from the simulation scheme, we also employed a forecasting scheme by using the antecedent observed discharge values in addition to P, T, and PET. We discussed performances of models in simulation and forecasting of streamflow regarding model types, input structures and catchment dynamics in detail.

中文翻译：

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使用机器学习模型与基流分离相结合的水流模拟和预测

摘要 由于大量相互关联的水文过程，降雨-径流关系的有效模拟是最复杂的问题之一。众所周知，在没有先行流量值的情况下，机器学习模型可能无法仅从气象变量模拟流量。造成这种情况的主要原因可能是流量和气象变量之间的关系低且滞后。为了克服这种低效率，我们首次通过将基流分离方法与三种机器学习方法相结合来开发模拟框架。结果表明，将水流分成不同的组成部分，例如基流和地表流，可用于提高机器学习模型的模拟和预测能力。我们使用支持向量回归 (SVR)、人工神经网络 (ANN) 和随机森林 (RF) 作为降水 (P)、温度 (T) 和潜在蒸散量 (PET) 的函数来模拟美国四条河流中的水流。我们得出结论，基流分离方法在一定程度上提高了机器学习模型的仿真性能。除了模拟方案外，我们还采用了预测方案，通过使用除 P、T 和 PET 之外的先行观测流量值。我们详细讨论了模型在模型类型、输入结构和流域动态方面的模拟和预测中的模型性能。温度 (T) 和潜在蒸散量 (PET)。我们得出结论，基流分离方法在一定程度上提高了机器学习模型的仿真性能。除了模拟方案外，我们还采用了预测方案，通过使用除 P、T 和 PET 之外的先行观测流量值。我们详细讨论了模型在模型类型、输入结构和流域动态方面的模拟和预测中的模型性能。温度 (T) 和潜在蒸散量 (PET)。我们得出结论，基流分离方法在一定程度上提高了机器学习模型的仿真性能。除了模拟方案外，我们还采用了预测方案，通过使用除 P、T 和 PET 之外的先行观测流量值。我们详细讨论了模型在模型类型、输入结构和流域动态方面的模拟和预测中的模型性能。