Runoff prediction method and system based on interpretable Bayesian gating circulation unit

By introducing an interpretable Bayesian Gated Cycle Unit (EB-GRU) model in hydrological runoff prediction, the shortcomings of existing models in terms of interpretability and uncertainty quantification are solved, and higher prediction accuracy and interpretability are achieved.

CN119599195APending Publication Date: 2025-03-11JIANGXI NORMAL UNIV
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Patent Information

Application Number
CN202411680544.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-22
Publication Date
2025-03-11

AI Technical Summary

Technical Problem

Existing deep learning models lack interpretability and uncertainty quantification capabilities in hydrological runoff prediction, making it difficult to obtain reliable prediction results, especially in complex and variable hydrological systems.

Method used

A runoff prediction method based on an interpretable Bayesian gated cycle unit (EB-GRU) model is proposed. Through Bayesian inference and SHAP methods, the GRU model is optimized to improve the interpretability and uncertainty quantization ability of the model.

Benefits of technology

The runoff prediction accuracy is improved, the uncertainty in the prediction process is effectively quantified, and the explanatory analysis of the contribution of hydrological meteorological factors is provided, which enhances the interpretability and practicality of the model.

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Abstract

The invention discloses a runoff prediction method and system based on an interpretable Bayesian gated circulation unit (GRU), and the method comprises the steps: firstly introducing Bayesian reasoning into the GRU, constructing a new model for runoff prediction, carrying out the effective prediction of runoff, and quantifying the uncertainty of runoff prediction, and then, a prediction result of the newly built model is explained by using a SHAPs (SHapley Address Explanation) method, and the importance of the hydrometeorological indexes on runoff prediction is analyzed. The method has high practicability and generalizability, the precision is improved, meanwhile, the theoretical framework of runoff prediction is widened, the uncertainty in runoff prediction is quantified, the interpretability of the model is improved, and technical support can be provided for optimized resource allocation and reservoir operation for water resource management of a drainage basin.
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