ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2025
Challenges and opportunities of ML and explainable AI in large-sample hydrology.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.Environmental science & technology · 2026Review
- Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction.Scientific reports · 2026Article
- Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning.PloS one · 2026Article
- New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations.NPJ climate and atmospheric science · 2026Review
- Adapting water resource systems to a changing future: challenges for UK hydrology in the 21st century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Hydrology in the 21st century: challenges in science, to policy and practice.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Why people matter in 21st century hydrology: lessons learnt from flood forecasting and warning.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
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'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.