A graph convolutional neural network water level prediction method based on geographical optimal similarity

By constructing an adjacency matrix based on Geographic Optimal Similarity (GOS) and combining it with a Graph Convolutional Neural Network (GCN), the problem of insufficient spatial correlation capture in traditional water level prediction is solved, achieving higher accuracy and robustness in water level prediction.

CN122364831APending Publication Date: 2026-07-10JIANGXI ACAD OF ECO-ENVIRONMENTAL SCI & PLANNING +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGXI ACAD OF ECO-ENVIRONMENTAL SCI & PLANNING
Filing Date
2026-05-19
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively capture the complex nonlinear spatial correlations between hydrological stations. Traditional adjacency matrices, constructed based on geographical distance or simple river network topology, cannot accurately reflect the implicit connections in actual hydrological processes, thus limiting the accuracy of water level prediction.

Method used

The similarity between sites is calculated using the Geographic Optimal Similarity (GOS) principle, an adjacency matrix is ​​constructed, and a graph convolutional neural network (GCN) is used for temporal state updates. Multi-dimensional environmental similarity is captured through multi-kernel graph convolutional layers and gated recurrent mechanisms to construct a graph structure network suitable for river network topology.

Benefits of technology

It significantly improves the accuracy and robustness of water level prediction, can better quantify the multidimensional environmental similarity between stations, overcomes the problems of single spatial feature extraction and loose spatiotemporal coupling in traditional models, and meets the prediction needs under real-time and complex conditions.

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Abstract

This invention discloses a graph convolutional neural network (GCN) method for water level prediction based on geographic optimal similarity. The method includes: acquiring hydrological monitoring data from multiple stations and related explanatory variables, and preprocessing the data; calculating the geographic optimal similarity (GOS) between stations based on the historical statistical characteristics of the explanatory variables of each station, and constructing a dynamic adjacency matrix reflecting nonlinear hydrological associations; using this adjacency matrix as the graph structure of a graph convolutional neural network (GCN) to construct a GOS-GCN model for water level prediction; training and testing the GOS-GCN model using real hydrological data, and evaluating the prediction results. This invention effectively solves the limitations of relying solely on geographic distance for mapping, and significantly improves the prediction accuracy and robustness of the model under complex spatiotemporal conditions.
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