Machine learning for hydrologic sciences: An introductory overview
Machine learning for hydrologic sciences: An introductory overview
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DOI：
10.1002/wat2.1533
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Tianfang Xu;F. Liang
中科院分区：
文献类型：
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作者：
Tianfang Xu;F. Liang
近年来，水文社区对机器学习的兴趣激增。这种兴趣主要是由快速增长的水文数据存储库以及机器学习在各种学术和商业应用中的成功所驱动的，现在由于硬件和软件的可访问性增加而成为可能。本概述适用于机器学习领域的新读者。它在历史背景下对常用的机器学习算法和深度学习架构进行了非技术性介绍。在水文科学的应用进行了总结，重点是最近的研究。它们包括模式和事件的检测，如土地利用变化，水文变量和过程的近似，如降雨径流建模，以及挖掘变量之间的关系，以确定控制因素。机器学习的使用也在与基于过程的建模集成的背景下进行了讨论，用于参数化，替代建模和偏差校正。最后，文章强调了水文应用中外推鲁棒性、物理可解释性和小样本量的挑战。
The hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non‐technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall‐runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process‐based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications.