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Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in differen...

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Autores principales: Cao, Ying, Yin, Kunlong, Zhou, Chao, Ahmed, Bayes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038680/
https://www.ncbi.nlm.nih.gov/pubmed/32033307
http://dx.doi.org/10.3390/s20030845
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author Cao, Ying
Yin, Kunlong
Zhou, Chao
Ahmed, Bayes
author_facet Cao, Ying
Yin, Kunlong
Zhou, Chao
Ahmed, Bayes
author_sort Cao, Ying
collection PubMed
description The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.
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spelling pubmed-70386802020-03-09 Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis Cao, Ying Yin, Kunlong Zhou, Chao Ahmed, Bayes Sensors (Basel) Article The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA. MDPI 2020-02-05 /pmc/articles/PMC7038680/ /pubmed/32033307 http://dx.doi.org/10.3390/s20030845 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cao, Ying
Yin, Kunlong
Zhou, Chao
Ahmed, Bayes
Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title_full Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title_fullStr Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title_full_unstemmed Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title_short Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
title_sort establishment of landslide groundwater level prediction model based on ga-svm and influencing factor analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038680/
https://www.ncbi.nlm.nih.gov/pubmed/32033307
http://dx.doi.org/10.3390/s20030845
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