Slater Louise, Blougouras Georgios, Deng Liangkun, Deng Qimin, Ford Emma, Hoek van Dijke Anne, Huang Feini, Jiang Shijie, Liu Yinxue, Moulds Simon, Schepen Andrew, Yin Jiabo, Zhang Boen
School of Geography and the Environment, University of Oxford, Oxford, UK.
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Thüringen, Germany.
Philos Trans A Math Phys Eng Sci. 2025 Jul 31;383(2302):20240287. doi: 10.1098/rsta.2024.0287.
Machine learning (ML) is a powerful tool for hydrological modelling, prediction, dataset creation and the generation of insights into hydrological processes. As such, ML has become integral to the field of large-sample hydrology, where hundreds to thousands of river catchments are included within a single ML model to capture diverse hydrological behaviours and improve model generalizability. This manuscript outlines recent advances in ML for large-sample hydrology. We review new tools in explainable AI (XAI) and interpretability approaches, as well as challenges in these areas. Key research avenues for large-sample hydrology include addressing variability in interpretations resulting from different ML models and XAI techniques, enhancing hydrological predictions in data-sparse and human-impacted regions, reducing the 'cascade of uncertainty' inherent in hydrological modelling, developing improved methods for multivariate prediction and identifying causal relationships.This article is part of the discussion meeting issue 'Hydrology in the 21st century: challenges in science, to policy and practice'.
机器学习（ML）是水文建模、预测、数据集创建以及深入了解水文过程的强大工具。因此，ML已成为大样本水文学领域不可或缺的一部分，在该领域中，单个ML模型纳入了数百至数千个河流流域，以捕捉不同的水文行为并提高模型的通用性。本手稿概述了大样本水文学中ML的最新进展。我们回顾了可解释人工智能（XAI）中的新工具和可解释性方法，以及这些领域中的挑战。大样本水文学的关键研究方向包括解决不同ML模型和XAI技术导致的解释变异性、加强数据稀疏和受人类影响地区的水文预测、减少水文建模中固有的“不确定性级联”、开发改进的多变量预测方法以及识别因果关系。本文是“21世纪的水文学：科学、政策和实践中的挑战”讨论会议议题的一部分。
