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September 10, 2025Hydrology and earth system sciencesOpen Access

A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling

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Authors

NHNgo Nghi Truyen HuynhAix-Marseille Université PGPierre‐André GaramboisCentre National de la Recherche Scientifique BRBenjamin RenardInstitut National de la Recherche Agronomique

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Implication

This approach combines artificial intelligence and hydrological modeling to improve flood predictions, suggesting better accuracy in diverse conditions.

Key Points

Hybrid models achieve superior accuracy and robustness compared to classical approaches in flood modeling.
Using two high-resolution datasets, the framework showed enhanced interpretability of hydrological parameters.
The hybrid framework refines internal water fluxes, contributing to advanced flood modeling efforts.
Results indicate the framework's adaptability to complex hydrological responses and its potential for uncovering physical processes.

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Huynh et al. (2025) studied this question.

synapsesocial.com/papers/68c1c9dd54b1d3bfb60f306f https://doi.org/10.5194/hess-29-3589-2025

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Implication

This approach combines artificial intelligence and hydrological modeling to improve flood predictions, suggesting better accuracy in diverse conditions.

A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling

Discussion

Member takes

Implication

Key Points

Cite This Study

A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling

Discussion

Member takes

Implication

Key Points

Cite This Study

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