Scientific machine learning in Hydrology: a unified perspective

• URL: http://arxiv.org/abs/2506.06308v1
• Date: Sat, 24 May 2025 15:21:10 GMT
• Title: Scientific machine learning in Hydrology: a unified perspective
• Authors: Adoubi Vincent De Paul Adombi,
• Abstract summary: Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling.<n>Multiple methodological families have emerged, including physics-informed machine learning, physics-guided machine learning, hybrid physics-machine learning, and data-driven physics discovery.<n>This review proposes a unified methodological framework for each SciML family, bringing together contributions into a coherent structure that fosters conceptual clarity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling, offering significant potential for advancing hydrological research. In recent years, multiple methodological families have emerged, including physics-informed machine learning, physics-guided machine learning, hybrid physics-machine learning, and data-driven physics discovery. Within each of these families, a proliferation of heterogeneous approaches has developed independently, often without conceptual coordination. This fragmentation complicates the assessment of methodological novelty and makes it difficult to identify where meaningful advances can still be made in the absence of a unified conceptual framework. This review, the first focused overview of SciML in hydrology, addresses these limitations by proposing a unified methodological framework for each SciML family, bringing together representative contributions into a coherent structure that fosters conceptual clarity and supports cumulative progress in hydrological modeling. Finally, we highlight the limitations and future opportunities of each unified family to guide systematic research in hydrology, where these methods remain underutilized.

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