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Landslide Displacement Prediction Based on Multivariate LSTM Model
There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of la...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859347/ https://www.ncbi.nlm.nih.gov/pubmed/36673921 http://dx.doi.org/10.3390/ijerph20021167 |
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author | Duan, Gonghao Su, Yangwei Fu, Jie |
author_facet | Duan, Gonghao Su, Yangwei Fu, Jie |
author_sort | Duan, Gonghao |
collection | PubMed |
description | There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide. |
format | Online Article Text |
id | pubmed-9859347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98593472023-01-21 Landslide Displacement Prediction Based on Multivariate LSTM Model Duan, Gonghao Su, Yangwei Fu, Jie Int J Environ Res Public Health Article There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide. MDPI 2023-01-09 /pmc/articles/PMC9859347/ /pubmed/36673921 http://dx.doi.org/10.3390/ijerph20021167 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Duan, Gonghao Su, Yangwei Fu, Jie Landslide Displacement Prediction Based on Multivariate LSTM Model |
title | Landslide Displacement Prediction Based on Multivariate LSTM Model |
title_full | Landslide Displacement Prediction Based on Multivariate LSTM Model |
title_fullStr | Landslide Displacement Prediction Based on Multivariate LSTM Model |
title_full_unstemmed | Landslide Displacement Prediction Based on Multivariate LSTM Model |
title_short | Landslide Displacement Prediction Based on Multivariate LSTM Model |
title_sort | landslide displacement prediction based on multivariate lstm model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859347/ https://www.ncbi.nlm.nih.gov/pubmed/36673921 http://dx.doi.org/10.3390/ijerph20021167 |
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