Lake area water quality change forecasting and early warning method based on lake flow-water quality complex relation

CN121935802AActive Publication Date: 2026-04-28NANJING HYDRAULIC RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING HYDRAULIC RES INST
Filing Date
2026-03-27
Publication Date
2026-04-28

AI Technical Summary

Technical Problem

Existing lake water quality early warning technologies suffer from problems such as prediction distortion due to the lack of physical mechanism constraints and insufficient spatial linkage, resulting in delayed early warnings.

Method used

By acquiring multi-source monitoring data of the lake area, calculating the comprehensive characteristic index of the lake flow, identifying the time lag relationship between it and water quality monitoring data, constructing a lake flow-water quality response model, dynamically adjusting the early warning threshold, and performing sliding window analysis and fusion decision-making to generate water quality early warning information.

Benefits of technology

It enables accurate early warning of gradual and sudden water quality risks, solves the problems of data models violating physical laws and single-point early warning lag, and improves the timeliness and accuracy of early warning.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention discloses a lake water quality change forecasting and early warning method based on a lake flow-water quality complex relation. The method comprises the following steps: acquiring lake region multi-source monitoring data, and calculating lake flow comprehensive characteristic indexes including flow velocity, vorticity and hydraulic retention time; constructing a conventional early warning path, identifying the optimal time lag of the lake flow and the water quality, constructing a response model by adopting physical constraint kernel regression introducing transportation and retention constraints, and determining a dynamic early warning threshold value changing along with the lake flow condition according to the response model; constructing a sudden change early warning path, calculating a critical moderation index of a water quality sequence, constructing a spatial propagation weight based on a flow direction of a flow field, and superposing an upstream signal to a downstream to synthesize a spatial enhanced early warning index; and carrying out fusion decision on the early warning levels of the two paths. According to the method, the problems that a data model violates a physical rule and single-point early warning lags are solved, and early warning of gradual change type and sudden change type water quality risks is achieved.
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Citation Information

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