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Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability

Lu, Dan·Yang, Tiantian·Liu, Xiaofeng
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Abstract

The growing influence of machine learning (ML) in every aspect of our lives has led to revolutionary advancements in our understanding, prediction, and decision-making capabilities. One field that stands to benefit greatly from applying these techniques includes hydrology and hydraulics. The ability to predict hydrological and hydraulic phenomena with greater accuracy and reliability is of utmost importance, given the increasing threats posed by climate change and extreme weather/climate events. In this editorial, we explore the significant contributions made by four recent studies that aim to advance hydrological and hydraulic predictability through data-driven ML.

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42 ENGINEERINGclimate changedata-driven machine learningexplainable machine learninghydrological and hydraulic predictabilityuncertainty quantification

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Lu, Dan, Yang, Tiantian, Liu, Xiaofeng. 2023-06-01. Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability. https://doi.org/10.3389/frwa.2023.1215966

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