Regional geological disaster susceptibility prediction method and device based on machine learning

A technology of machine learning and geological disasters, applied in geographic information databases, special data processing applications, structured data retrieval, etc., can solve problems such as low prediction accuracy, achieve the effect of improving prediction accuracy and alleviating low prediction accuracy

Active Publication Date: 2019-07-12
杭州鲁尔物联科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a method and device for predicting the susceptibility of regional geological disasters based on machine learning, so as to alleviate the technical problems of low prediction accuracy existing in the prior art and improve the prediction accuracy

Method used

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  • Regional geological disaster susceptibility prediction method and device based on machine learning
  • Regional geological disaster susceptibility prediction method and device based on machine learning
  • Regional geological disaster susceptibility prediction method and device based on machine learning

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Embodiment 1

[0059] The embodiment of the present invention provides a method for predicting the susceptibility of regional geological disasters based on machine learning, which can be applied to regional prediction and evaluation of geological disasters such as landslides, debris flows, and collapses.

[0060] Such as figure 1 As shown, the method includes the following steps:

[0061] Step S101 , acquiring monitoring data of preset collection parameters of individual monitoring points in the target area within a preset time period.

[0062] The preset time period here may be a period of time in the past or a period of time in the future.

[0063] Specifically, the step S101 is mainly realized through the following steps:

[0064] 1. Determine the target area and target geological hazard type;

[0065] Wherein, the target area refers to any area that needs to be monitored, for example, it can be the southwest region, the northeast region, etc., or it can be a province, city, town, etc....

Embodiment 2

[0123] refer to figure 2 , On the basis of Embodiment 1, the embodiment of the present invention provides another method for predicting the susceptibility of regional geological disasters based on machine learning. The difference from Embodiment 1 is that the method also includes:

[0124] Step S201, obtaining historical data in the national geological disaster professional monitoring database;

[0125] Specifically, investigate all domestic geological disaster events with data records, summarize all monitoring data across the country, and establish a national geological disaster professional monitoring database;

[0126] It should be pointed out that all monitoring data are used as data samples of the geological disaster susceptibility prediction model, and the input parameters include inducing factors (meteorological data, seismic data), topographic data, deformation monitoring data, etc., and the prediction target is geological disasters in different regions probability o...

Embodiment 3

[0184] Such as Figure 4 As shown, the embodiment of the present invention provides a machine learning-based regional geological disaster susceptibility prediction device, including:

[0185] An acquisition module 100, configured to acquire monitoring data of preset acquisition parameters of each individual monitoring point in the target area within a preset time period;

[0186] Wherein, the acquisition module 100 is specifically used to determine the target area and the target geological hazard type; select and set a plurality of individual monitoring points in the target area; set presets at each of the individual monitoring points based on the target geological hazard type A sensor group: using the preset sensor group of each individual monitoring point to collect data on preset acquisition parameters to obtain monitoring data within a preset time period of each individual monitoring point.

[0187] A processing module 200, configured to preprocess the monitoring data of ea...

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Abstract

The invention provides a regional geological disaster susceptibility prediction method and device based on machine learning, relates to the field of geological analysis, and aims to alleviate the technical problem of low prediction precision in the prior art and improve the prediction precision. The method comprises the following steps: acquiring monitoring data of preset acquisition parameters ofeach monomer monitoring point in a target area in a preset time period; preprocessing the monitoring data of each single monitoring point to obtain standardized data of each single monitoring point;performing feature engineering on the standardized data of each single monitoring point to obtain training parameter data of each single monitoring point; performing monomer index prediction on each monomer monitoring point by utilizing a preset machine learning method based on the training parameter data of each monomer monitoring point to obtain a monomer prediction result of each monomer monitoring point; and integrating the monomer prediction results of the monomer monitoring points to obtain a regional prediction result of the target area.

Description

technical field [0001] The invention relates to the technical field of geological analysis and evaluation, in particular to a method and device for predicting regional geological disaster susceptibility based on machine learning. Background technique [0002] In recent years, geological disasters have occurred frequently, which have had a huge impact on the personal safety of residents, transportation, water conservancy and hydropower, and industrial factories and mines. [0003] The research on short-term early warning and forecasting of geological disasters is very in-depth. On the basis of early warning and forecasting, many geological disasters can be avoided, avoiding a large number of casualties and property losses. However, it is not enough just to avoid disasters. It is more important to deploy geological disaster prevention and mitigation work in advance. Therefore, it is necessary to carry out geological disaster prediction. Geological disaster prediction is the ba...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/29G06F16/21
CPCG06F16/21G06F16/29
Inventor 胡辉宋杰董梅张亮
Owner 杭州鲁尔物联科技有限公司
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