Space-time joint modeling system and method for watershed water quality prediction

By building a joint modeling system of space-time and time-series and spatial coding, a dynamic graph learner is used to process the basin topology, the problem of space-time coupling impact in water quality prediction in basin is solved, and more accurate water quality prediction and visualization of pollutant diffusion paths are achieved.

CN120373544AActive Publication Date: 2025-07-25SUN YAT SEN UNIV +1
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Patent Information

Application Number
CN202510455814.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-11
Publication Date
2025-07-25
Estimated Expiration
2045-04-11

AI Technical Summary

Technical Problem

When processing spatiotemporal data, the existing basin water quality prediction model ignores the spatiotemporal coupling effect of basin topology on pollutant diffusion, resulting in insufficient spatial correlation modeling and difficult to balance the complexity and computational efficiency of the prediction model.

Method used

A joint modeling system for water quality prediction in the basin is designed. Through the data acquisition module, feature extraction module, feature fusion module and model training module, combined with timing encoding and spatial encoding, dynamic graph learners are used to carry out dynamic graph learning, and a gated network for dynamic fusion of spatiotemporal features is constructed, taking into account the impact of the basin topology on the spatiotemporal coupling of pollutant diffusion.

Benefits of technology

It realizes more accurate water quality prediction in the basin, can dynamically adjust attention weights, pay attention to the propagation of information at key hub points, suppress noise or redundant information, and improves the accuracy and reliability of the prediction model.

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Abstract

The invention discloses a spatial-temporal joint modeling system and method for watershed water quality prediction. The system comprises a data acquisition module, a feature extraction module, a feature fusion module, a model training module and a prediction output module. In the watershed water quality prediction process, the combined influence of time and space is considered, a space-time position coding combined embedded layer is designed, an attention mechanism guided by hydrological characteristics is combined, a door control network dynamically fused with space-time characteristics is constructed, and the space-time coupling influence of a watershed topological structure on pollutant diffusion is considered; the attention weight is dynamically adjusted by quantifying the topological importance of the monitoring points in the network, a feature channel which is most effective for a current prediction task is highlighted, and noise or redundant information is suppressed; in the model training process, a simplified gating mechanism is adopted, the gradient dispersion problem is reduced, the nonlinearity of a layer is kept, convergence is accelerated, a dynamic graph learning device is used, physical rules are respected, data changes are self-adapted, and more accurate water quality modeling is achieved.
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