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Landslide short-term and temporary intelligent early warning method based on XGBoost algorithm

A short-term, intelligent technology, applied in the field of landslide monitoring and early warning and machine learning, can solve the problems of long training time and poor prediction accuracy, and achieve the goal of improving the prediction method, improving the prediction speed and prediction accuracy, and improving the calculation speed and prediction accuracy Effect

Pending Publication Date: 2020-10-16
中国地质环境监测院
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Problems solved by technology

It can solve the problems of long training time and poor prediction accuracy of existing machine learning models in the intelligent prediction of landslide deformation

Method used

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  • Landslide short-term and temporary intelligent early warning method based on XGBoost algorithm
  • Landslide short-term and temporary intelligent early warning method based on XGBoost algorithm
  • Landslide short-term and temporary intelligent early warning method based on XGBoost algorithm

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Embodiment Construction

[0028] This paper takes the monitoring data of the crack meter installed on the landslide as an example. The specific implementation includes the following steps. This method can be extrapolated to the GNSS surface absolute displacement, inclinometer, accelerometer and other sensors installed on the landslide to monitor the surface deformation of the landslide. Prediction of collected data.

[0029]Step 1, deploy monitoring sensors on the landslide body to collect the crack amount, rainfall and soil moisture content of the landslide body in real time.

[0030] Step 2. Perform data preprocessing on the monitored crack volume, rainfall and soil moisture content, including eliminating outliers and supplementing missing values; when supplementing missing values, use the classic time series analysis model ARIMA to fit the monitoring data to obtain missing values. The deformation data at each moment, after data filling, the first-order difference sequence of the landslide deformatio...

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Abstract

The invention discloses a landslide short-term and temporary intelligent early warning method based on an XGBoost algorithm, and relates to the field of landslide monitoring and early warning and machine learning. The XGBoost algorithm is applied to the field of landslide short-term and temporary intelligent early warning for the first time. The method specifically comprises the following steps: acquiring landslide body feature data in real time to construct landslide body feature vectors, wherein the landslide body feature vectors comprise rainfall, soil moisture content and landslide body surface deformation features; allowing the XGBoost model to predict the landslide body surface deformation characteristics of the prediction day according to the landslide body characteristic vectors constructed by the historical time sequence before the prediction day and the rainfall predicted by the weather forecast of the prediction day; and if the prediction value is greater than the safety threshold, sending out intelligent early warning. According to the method, the calculation speed and the prediction precision are obviously improved, and intelligent short-term and temporary prediction can be carried out on landslide deformation based on future rainfall prediction data.

Description

technical field [0001] The invention discloses an XGBoost algorithm-based intelligent early warning method for landslide short-term imminence, and relates to the fields of landslide monitoring and early warning and machine learning. Background technique [0002] As an important geological disaster, landslides exist widely in our country and occur frequently, causing large casualties and property losses. The disaster situation is still severe at present. With the rapid development of science and technology, monitoring and early warning has become the most direct and effective means to actively prevent landslide geological disasters. Landslide is a complex multi-dimensional nonlinear dynamic system affected by its own geological conditions and external factors. Landslide deformation is the result of multiple factors in this complex multi-dimensional nonlinear dynamic system. It is a tool that can directly reflect the evolution process of landslide deformation. a composite var...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G08B21/10G06N20/00G06N5/00
CPCG06Q10/04G06Q50/26G08B21/10G06N20/00G06N5/01
Inventor 赵文祎张鸣之马娟齐干邢顾莲朱赛楠
Owner 中国地质环境监测院
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