Abstract Low-flow estimation at ungagged sites is a challenging task. Ensemble-based machine learning regression has recently been utilized in modeling hydrologic phenomena and showed improved performance compared to classical regional regression approaches. Ensemble modeling mainly revolves around developing a proper training framework of the individual learners and combiners. An ensemble framework is proposed in this study to drive the generalization ability of the sub-ensemble models and the ensemble combiners. Information mixtures between the subsamples are introduced and, unlike common ensemble frameworks, are explicitly devoted to the ensemble members as well as ensemble combiners. The homogeneity paradigm is developed via a two-stage resampling approach, which creates sub-samples with controlled information mixture levels for the training of the individual learners. Artificial neural networks are used as sub-ensemble members in combination with a number of ensemble integration techniques. The proposed model is applied to estimate summer and winter low-flow quantiles for catchments in the province of Quebec, Canada. The results provide significant improvement when compared to the other models presented in the literature. The results of the homogeneity levels from the optimum ensemble models demonstrate the importance of utilizing the diversity concept in ensemble learning applications.

中文翻译：

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多样性驱动的基于人工神经网络的集成框架，用于未测量站点的季节性低流量分析

摘要 无标记站点的低流量估计是一项具有挑战性的任务。基于集成的机器学习回归最近被用于水文现象建模，与经典的区域回归方法相比，性能有所提高。集成建模主要围绕为个体学习者和组合者开发适当的培训框架。本研究提出了一个集成框架，以驱动子集成模型和集成组合器的泛化能力。引入了子样本之间的信息混合，并且与常见的集成框架不同，它明确地用于集成成员以及集成组合器。同质性范式是通过两阶段重采样方法开发的，它创建了具有受控信息混合水平的子样本，用于训练个体学习者。人工神经网络与许多集成集成技术结合用作子集成成员。建议的模型用于估计加拿大魁北克省集水区的夏季和冬季低流量分位数。与文献中提出的其他模型相比，结果提供了显着的改进。最佳集成模型的同质性水平结果证明了在集成学习应用中利用多样性概念的重要性。建议的模型用于估计加拿大魁北克省集水区的夏季和冬季低流量分位数。与文献中提出的其他模型相比，结果提供了显着的改进。最佳集成模型的同质性水平结果证明了在集成学习应用中利用多样性概念的重要性。建议的模型用于估计加拿大魁北克省集水区的夏季和冬季低流量分位数。与文献中提出的其他模型相比，结果提供了显着的改进。最佳集成模型的同质性水平结果证明了在集成学习应用中利用多样性概念的重要性。