Mountain torrent debris flow early warning system based on multi-source data comprehensive analysis

A multi-source data integration method for mudslide prediction addresses the limitations of existing models by using deformation and rainfall monitoring, along with historical data, to build a geology-weather-hydrology coupled model, improving prediction accuracy and enabling timely warnings.

CN120318991APending Publication Date: 2025-07-15陈建君
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Patent Information

Application Number
CN202510513004.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-23
Publication Date
2025-07-15

AI Technical Summary

Technical Problem

The existing technology has inadequate monitoring in the early warning of mountain torrent mudslide disasters and cannot provide sufficient decision-making support for threatened areas. In addition, the false alarm and misreport of a single early warning equipment are serious, making it difficult to accurately determine the risk of time and space disasters of mudslides.

Method used

The comprehensive multi-source data analysis method is adopted to build a fully coupled mountain torrent mudslide early warning model, using rainfall, source deformation and mud spot monitoring data, combining historical disaster data and multi-model coordination, dynamically adjust thresholds, eliminate outliers, realize intelligent closed-loop management of data, improve early warning accuracy, and realize cross-basin collaborative early warning through blockchain technology.

Benefits of technology

It has achieved accurate warnings for mountain torrents and mudslide disasters, reduced false alarms and missed reports, provided timely risk aversion measures, improved the credibility and accuracy of the early warning system, and ensured that the decision-making process is not tampered with.

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Abstract

The invention discloses a mountain torrent debris flow early warning method and system based on multi-source data comprehensive analysis. And the monitoring system carries out multi-source monitoring on rainfall, material source and mud level change characteristics of the ditch domain. The method is based on double-threshold rainfall intensity parameters, a quantitative analysis model of channel mud level and flow, and a blocking and collapsing mechanism of object source deformation. The threshold value of each element is determined according to the disaster history, a disaster outbreak probability formula is established, and key factor weights are screened through a random forest algorithm. And data preprocessing is carried out through cross validation and dynamic adjustment. And substituting monitoring data into the analysis model, and comprehensively judging the early warning level of the mountain torrent debris flow. And issuing an early warning signal of a corresponding level to the object in the threatened area through the early warning system. After operation, an incremental random forest method is adopted to update the model through online learning, data redisk is carried out, and model threshold correction and parameter inversion analysis are carried out. According to the pre-warning system, a multi-index combined action comprehensive monitoring and pre-warning evaluation system for quantitative analysis is established.
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