A Method and System for Predicting Sediment Concentration Based on Freeze-Thaw Coupling Physical Constraint Model

By constructing a freeze-thaw coupled physical constraint model, combined with a benchmark sediment concentration physical model and an artificial neural network, the problems of model applicability and physical constraints in sediment concentration prediction were solved, achieving high-precision and robust sediment concentration prediction.

CN121480322BActive Publication Date: 2026-06-30CHINA AGRI UNIV
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Patent Information

Application Number
CN202511871400.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-12
Publication Date
2026-06-30
Estimated Expiration
2045-12-12

AI Technical Summary

Technical Problem

Existing technologies are insufficient to solve the problem of predicting sediment concentration during freeze-thaw processes. Existing technologies in the field of sediment concentration prediction suffer from problems such as poor applicability of traditional models, lack of correlation between physical model parameters and freeze-thaw processes, and lack of physical constraints in data-driven models.

Method used

A sediment concentration prediction method based on a freeze-thaw coupling physical constraint model is adopted. By constructing a benchmark sediment concentration physical model and combining it with an artificial neural network (ANN), sediment mass conservation constraints and freeze-thaw weighting strategies are introduced to enhance the loss function and improve the stability and accuracy of the model under extreme conditions.

Benefits of technology

It improves the accuracy and robustness of sediment concentration prediction, ensures that the prediction results conform to physical laws, avoids negative values ​​or unreasonable extreme values, and enhances the response capability to freeze-thaw cycles and extreme runoff events.

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Abstract

This disclosure provides a method and system for predicting sediment concentration based on a freeze-thaw coupled physical constraint model, relating to the field of sediment concentration prediction technology. The method includes: acquiring and preprocessing various raw data such as hydrological data, meteorological data, and soil and vegetation data; constructing a baseline sediment concentration physical model based on the preprocessed raw data, and constructing an input feature vector; inputting the input feature vector into a sediment concentration prediction ANN model, and outputting the sediment concentration; wherein, during the training process of the sediment concentration prediction ANN model, the baseline sediment prediction of the physical model and the sediment mass conservation constraint are introduced into the ANN model loss function, and a freeze-thaw weighted strategy is used to enhance the loss function, resulting in a sediment concentration prediction ANN model that deeply integrates the physical model and the ANN model. This disclosure improves the stability and accuracy of predictions under extreme rainfall, runoff surges, or frequent freeze-thaw cycles.
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