水文领域的物理引导机器学习方法
Physics Guided Machine Learning Methods for Hydrology
AI总结：
针对径流预测的物理模型性能差距问题，提出将水文过程理解融入机器学习的方法，通过多任务学习框架显式建模中间过程，仅需天气驱动因子即可预测径流，在SWAT模拟数据集上验证了有效性且适用于多流域场景。
摘要：
由于径流生成背后多种非线性物理机制之间的复杂相互作用，径流预测是水文学领域的关键挑战之一。尽管基于物理的模型植根于对物理过程的深入理解，但仍存在显著的性能差距，这一差距可能通过利用机器学习的最新进展来解决。本研究的目标是将我们对水文过程和约束的理解融入机器学习算法中，以提高预测性能。针对该问题的传统机器学习模型使用天气驱动因子作为输入来预测径流。然而，从天气驱动因子生成径流涉及多个相互作用的中间过程。本方法的核心思想是使用多任务学习框架显式建模这些连接天气驱动因子和径流的中间过程。我们提出的方法在训练期间需要中间过程的数据，但在测试阶段仅需天气驱动因子即可预测径流。我们在由SWAT模型生成的模拟数据集上评估了该方法的有效性，该数据集针对明尼苏达州东南部Root河流域南支流的一个流域。虽然本文的重点是利用单个流域的数据提高性能，但此处提出的方法适用于基于机器学习的方法，这些方法使用多个流域的数据来提高每个单个流域的性能。
英文摘要：
Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich understanding of the physical processes, a significant performance gap still remains which can be potentially addressed by leveraging the recent advances in machine learning. The goal of this work is to incorporate our understanding of hydrological processes and constraints into machine learning algorithms to improve the predictive performance. Traditional ML models for this problem predict streamflow using weather drivers as input. However there are multiple intermediate processes that interact to generate streamflow from weather drivers. The key idea of the approach is to explicitly model these intermediate processes that connect weather drivers to streamflow using a multi-task learning framework. While our proposed approach requires data about intermediate processes during training, only weather drivers will be needed to predict the streamflow during testing phase. We assess the efficacy of the approach on a simulation dataset generated by the SWAT model for a catchment located in the South Branch of the Root River Watershed in southeast Minnesota. While the focus of this paper is on improving the performance given data from a single catchment, methodology presented here is applicable to ML-based approaches that use data from multiple catchments to improve performance of each individual catchment.
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