A residual water level calculation method, system, device and medium for a virtual tide station

By fusing spatiotemporal features through a hybrid neural network model, the problems of unfused spatiotemporal features and weak generalization ability in the residual water level estimation of virtual tide gauge stations are solved, achieving high-precision and real-time residual water level estimation, especially in applications in sparse or data-free areas of physical tide gauge stations.

CN122241249APending Publication Date: 2026-06-19GUANGZHOU URBAN PLANNING & DESIGN SURVEY RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGZHOU URBAN PLANNING & DESIGN SURVEY RES INST
Filing Date
2026-02-28
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing methods for estimating residual water levels at virtual tide gauge stations fail to effectively integrate spatiotemporal characteristics, have weak generalization ability in unknown areas, and exhibit low accuracy and real-time performance in estimating residual water levels. In particular, when physical tide gauge stations are sparsely distributed or far from the coverage area of ​​model training data, the estimation error is large and it is difficult to respond quickly to sudden water level changes.

⚗Method used

A hybrid neural network model is adopted, combining convolutional neural networks, long short-term memory neural networks and location coding branches. A spatiotemporal data cube is constructed through multi-source data to extract time, space and location features. Attention weights are used to fuse features to realize the estimation of the residual water level of the target virtual tide gauge station.

🎯Benefits of technology

It improves the generalization ability and estimation accuracy of unknown areas, enhances the real-time response capability to sudden water level events, reduces the dependence on the data quality of a single site, and improves the robustness and estimation accuracy of the system.

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Abstract

This invention discloses a method, system, equipment, and medium for estimating the residual water level of a virtual tide gauge station. The method first acquires real-time measured total water level data, real-time astronomical tide forecast data, real-time climate reanalysis data, and pre-stored seabed topography data from each reference tide gauge station, as well as the spatial location information of each reference tide gauge station and the target virtual tide gauge station. Then, the data is preprocessed to obtain a spatiotemporal data cube and residual water level data for each reference tide gauge station. Finally, based on the spatial location information, the spatiotemporal data cube, and the residual water level data, a hybrid neural network model including convolutional neural network branches, long short-term memory neural network branches, position encoding branches, feature fusion layers, and decoding estimation layers is used to estimate the residual water level, obtaining the estimated residual water level sequence for the target virtual tide gauge station. This invention effectively integrates spatiotemporal features, improves the generalization ability for unknown areas, and thus enhances the accuracy and real-time performance of residual water level estimation.
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