Geological disaster prediction method, device and equipment

Abstract

The invention discloses a geological disaster prediction method, device and equipment, and the method comprises the steps of obtaining the monitoring data of a monitoring region, wherein the monitoring data comprises the spatial data and attribute information for describing geological disasters; performing preprocessing and feature engineering on the spatial data and the attribute information to determine a feature subset of the geological disaster; and establishing an artificial intelligence model based on the feature subset to determine the predicted occurrence probability of the geologicaldisaster of the monitoring area. The embodiment of the invention discloses the technical scheme which includes obtaining monitoring data, processing the data and subjecting to feature engineering, extracting the feature subsets related to the geological disasters, up/down sampling the unbalanced data, determining the occurrence probability of the geological disasters based on the artificial intelligence model, realizing real-time monitoring and automatic prediction of the occurrence probability of the disasters, and improving the prediction comprehensiveness and accuracy.

Description

technical field [0001] Embodiments of the present invention relate to the field of geographic information processing technology and artificial intelligence learning, and in particular to a geological disaster prediction method, device and equipment. Background technique [0002] China is a country prone to geological disasters. Collapses, landslides, and mud-rock flows are almost all over the mountainous and hilly areas of every province in the country, and tens of thousands to hundreds of thousands of new disaster points will appear every year. Nearly a thousand people die in geological disasters every year, and the direct economic loss is 8 billion to 10 billion yuan. The indirect loss caused by interrupting traffic and destroying production and living facilities is even more difficult to estimate. [0003] In recent years, the construction of prediction models driven by physics and data for landslides, coupled with timely and effective monitoring and prediction measures, ...

AI Technical Summary

Problems solved by technology

[0003] In recent years, the construction of prediction models driven by physics and data for landslides, coupled with timely and effective monitoring and prediction measures, have made relatively successful early warnings for many major landslide disasters, but the successful early warning rate is not ideal. The main problem is It lies in the success rate of investigation and identification of landslide hidden danger points

Due to the large monitoring range of geological hazards, strong concealment of hidden danger points, complex formation conditions, many influencing factors, and unstable measurement accuracy and real-time performance, the phenomenon of "missing investigation" and "multiple investigation" is still common in its investigation and identification work

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