Method and system for identifying groundwater nitrate pollution risk based on machine learning

By constructing a unified spatiotemporal index and a multi-task model, the problem of the separation between risk identification and source analysis in the risk identification of groundwater nitrate pollution was solved, the accuracy of risk identification and the reliability of source identification were improved, the dynamic updating of the model and refined governance were realized, and the ability to characterize the spatiotemporal migration relationship of groundwater pollution was enhanced.

CN122197694APending Publication Date: 2026-06-12BEIJING ACADEMY OF AGRICULTURE & FORESTRY SCIENCES
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING ACADEMY OF AGRICULTURE & FORESTRY SCIENCES
Filing Date
2026-02-25
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

Existing technologies for identifying nitrate pollution risks in groundwater suffer from problems such as a disconnect between risk identification and source analysis, insufficient migration constraints, and weak dynamic update capabilities. They are difficult to form a unified modeling chain, and they do not make sufficient use of groundwater migration mechanisms, lack directional constraints, and conventional static modeling is not adaptable to seasonal drift and out-of-distribution samples, making it difficult to meet the needs of dynamic monitoring and refined governance.

Method used

A unified spatiotemporal index is constructed to generate a unified sample set with sampling time stamps, spatial unit identifiers, and data quality markers. A direction-sensitive migration constraint tensor is generated based on the digital twin map of groundwater pollution migration. The source contribution vector and source confidence interval are obtained through source decomposition. A multi-task model is constructed to predict risk probability and invert source contribution. Out-of-distribution detection and conformal calibration are performed to achieve incremental model updates.

Benefits of technology

It improves the accuracy of risk identification and the reliability of source identification, enhances the ability to characterize the spatiotemporal migration relationship of groundwater pollution, improves the application efficiency in dynamic monitoring and refined governance scenarios, and ensures the continuous iteration and update of the model and the credibility of the results.

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Abstract

The application provides a machine learning-based groundwater nitrate pollution risk identification method and system. The method comprises: constructing a unified space-time index and generating a unified sample set; constructing a groundwater pollution migration digital twin atlas based on the unified sample set, and generating a direction-sensitive migration constraint tensor; performing source decomposition on samples with isotope detection data to obtain source contribution vectors and source confidence intervals, and generating a source soft supervision field; constructing a multi-task model and performing collaborative training to obtain joint inference results; performing out-of-distribution detection and conformal calibration on the joint inference results to generate a risk identification result set; generating an incremental sampling point set based on the risk identification result set, and annotating the incremental sampling data to the unified sample set to trigger model incremental updating to generate a groundwater nitrate pollution risk identification result. The application can improve risk identification accuracy, enhance source discrimination reliability, and improve model iteration updating efficiency.
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