Geological disaster prediction method and device based on machine learning and electronic device

Abstract

The invention provides a geological disaster prediction method and device based on machine learning, and an electronic device. The method comprises the steps of collecting early warning physical parameter combinations influencing prediction of any type of geological disasters until M days before a current moment to obtain an influence factor vector time sequence; and inputting the influence factor vector time sequence into any type of geological disaster prediction model, and outputting a description factor vector time sequence for predicting any type of geological disasters in the future N days, wherein any prediction model is obtained by training based on an influence factor vector time sequence and a description factor vector time sequence label of any type of disaster sample, the data set for training and testing is constructed based on historical data processing based on the size of a sliding window with the width of M+N before training, and the geological disaster types comprise a landslide type, a debris flow type and a collapse type. According to the method, the device and the electronic equipment provided by the invention, the accuracy of geological disaster prediction is improved, and the real-time performance of geological disaster prediction is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of geological disaster prediction, in particular to a machine learning-based geological disaster prediction method, device, electronic equipment and storage medium. Background technique [0002] The traditional geological disaster early warning method mainly adopts the method of real-time monitoring of the data of the high-incidence areas of geological disasters through the deployment of sensors, and the method of immediately calling the police if the monitoring data reaches the preset threshold. However, the traditional geological disaster early warning method has the following shortcomings: [0003] 1. The monitoring method is single: the method of setting a fixed threshold first, then monitoring the data, and finally resetting the fixed threshold based on the monitoring data feedback results has been adopted, and the monitoring method is too single; [0004] 2. Early warning lag: If the alarm thr...

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Problems solved by technology

[0008] The present invention provides a geological disaster prediction method, device and electronic equipment based on machine learning, which are used to solve the problem that the existing geological disaster early warning method has low accuracy, poor real-time performance, and too single monitoring means, which is still an urgent problem to be solved by those skilled in the art Defects, the prediction model based on machine learning training, the training set constructed by obtaining a large number of samples through the acquired historical data can guarantee the accuracy of the prediction model, and the real-time data collected into the prediction model can output the prediction results immediately to ensure the real-time performance of the prediction

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