Challenges and opportunities of ML and explainable AI in large-sample hydrology

Area of Science:

• Earth and Environmental Sciences
• Computer Science

Background:

• Machine learning (ML) is crucial for hydrological modeling, prediction, and generating insights.
• Large-sample hydrology utilizes ML models with numerous river catchments to understand diverse hydrological behaviors and enhance generalizability.

Purpose of the Study:

• To review recent advancements in ML applications for large-sample hydrology.
• To discuss new tools in explainable AI (XAI) and interpretability approaches within this field.
• To identify key research challenges and future directions in ML for large-sample hydrology.

Main Methods:

• Review of recent literature on ML in large-sample hydrology.
• Analysis of emerging explainable AI (XAI) and interpretability techniques.
• Identification of challenges and research gaps in the field.

Main Results:

• ML is integral to large-sample hydrology, improving modeling and prediction across diverse catchments.
• New XAI tools offer enhanced interpretability but present challenges in model variability and data scarcity.
• Key research avenues include improving predictions in data-sparse/impacted regions and reducing uncertainty.

Conclusions:

• Continued development of ML and XAI is vital for advancing hydrological science and practice.
• Addressing challenges in interpretation, data scarcity, and uncertainty is crucial for future research.
• ML offers significant potential for understanding and predicting hydrological processes in the 21st century.