A distributed flood forecasting model based on coupling of physical mechanism and neural network

By constructing a correspondence between watershed grids and neural networks in the flood forecasting model and explicitly incorporating physical mechanisms, the problem of insufficient ability of existing models to reflect dynamic changes in hydrological processes and spatial generalization is solved, thus achieving high-precision and interpretable flood forecasting.

CN122287699APending Publication Date: 2026-06-26HOHAI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-18
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing flood forecasting models are insufficient in reflecting the dynamic changes of hydrological processes and in terms of spatial generalization ability. Physical mechanism models lack clear physical constraints, data-driven models are prone to overfitting, and the physical consistency and practical application effect of coupled models need to be improved.

Method used

By establishing a one-to-one correspondence between watershed grid units and neural network units, a flood forecasting neural network consistent with the watershed spatial structure is constructed. Physical mechanisms are explicitly integrated, and by combining a distributed hydrological model and a neural network, hydrological model parameters are dynamically calculated to achieve adaptive adjustment of runoff generation and confluence processes. The model parameters are then optimized through a backpropagation algorithm.

Benefits of technology

It improves the accuracy and spatial generalization ability of flood forecasting, enhances the interpretability of the model, and improves the simulation capability and engineering applicability under complex hydrological conditions.

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Abstract

This invention discloses a distributed flood forecasting model based on the coupling of physical mechanisms and neural networks. This model explicitly embeds the watershed spatial structure and physical constraints into the neural network computation process, balancing physical consistency with data-driven capabilities. This improves flood forecasting accuracy, spatial generalization ability, and model interpretability, demonstrating significant engineering application value. By establishing a one-to-one correspondence between watershed grid units and neural network units, a flood forecasting neural network consistent with the watershed spatial structure is constructed, achieving explicit integration of physical mechanisms and thus enhancing flood forecasting accuracy, spatial generalization ability, and model interpretability.
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