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Landslide displacement dynamic prediction method based on multiple influence factors

A technology of impact factors and dynamic prediction, applied in special data processing applications, biological neural network models, instruments, etc., can solve problems such as information cannot be preserved, and achieve accurate prediction results

Pending Publication Date: 2021-01-26
XI'AN POLYTECHNIC UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a dynamic prediction method for landslide displacement based on multi-influencing factors, which solves the problem in the prior art that the information at a long time cannot be preserved

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  • Landslide displacement dynamic prediction method based on multiple influence factors
  • Landslide displacement dynamic prediction method based on multiple influence factors
  • Landslide displacement dynamic prediction method based on multiple influence factors

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Embodiment

[0093] 1) if image 3 As shown, the time series curves of cumulative displacement of landslides at 5 monitoring points (GXT1, GXT2, GXT3, GXT4, GXT5) from 2003 to 2007. Looking at the 5 monitoring points, it can be seen that the cumulative deformation and displacement of landslides fluctuate more from May to November each year. large, showing an overall increasing trend; Figure 4 As shown, it is the accumulated deformation displacement time and monthly rainfall data of the GXT4 monitoring point at the front edge of the landslide. It can be seen from the figure that in the rainy season, the deformation displacement of the landslide shows a nonlinear increase trend with the increase of rainfall intensity. For example, in 2004 1 From April to April, the total rainfall of the landslide was 246.8mm, the corresponding maximum increment of deformation displacement was 2.037mm, and the displacement rate was 0.679mm / month, but from May to November, the maximum increment of landslide d...

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Abstract

The invention discloses a landslide displacement dynamic prediction method based on multiple influence factors. The landslide displacement dynamic prediction method is specifically implemented according to the following steps of 1, decomposing a landslide cumulative deformation displacement time curve according to a time sequence addition model; 2, extracting trend term displacement from landslidecumulative deformation displacement time curve decomposition by adopting a moving average method; 3, predicting trend term displacement by adopting a cubic polynomial; 4, selecting a main influence factor from the predicted trend term displacement by adopting a grey correlation degree sieve and taking the main influence factor as an initial input vector of a deep learning LSTM neural network model to predict the landslide periodic term displacement; and 5, according to a time sequence decomposition principle, superposing the predicted values of the displacement sub-sequences to obtain a finalpredicted value of the displacement, thereby finishing the dynamic prediction of the landslide displacement, and solving the problem that information at a long ago moment cannot be reserved in the prior art.

Description

technical field [0001] The invention belongs to the technical field of landslide displacement prediction and relates to a dynamic prediction method for landslide displacement based on multiple influencing factors. Background technique [0002] Landslide disasters seriously threaten the safety of the country and the people, so landslide prediction and early warning is particularly important. Displacement, as a macroscopic characterization of landslide hazards, has long been a hotspot in landslide prediction and forecasting by scholars at home and abroad. At present, there are mainly methods for displacement prediction: 1) Decompose the cumulative deformation displacement of observation points in the study area into trend displacement affected by internal factors and periodic displacement or fluctuating displacement affected by external factors, and model them respectively Prediction, and finally add the above two prediction values ​​to get the cumulative displacement predict...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06K9/62G06F30/20
CPCG06N3/049G06F30/20G06N3/044G06F18/214
Inventor 李丽敏张明岳温宗周郭伏张俊何洋魏雄伟
Owner XI'AN POLYTECHNIC UNIVERSITY
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