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Comparative study on landslide susceptibility mapping based on unbalanced sample ratio
The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM f...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086050/ https://www.ncbi.nlm.nih.gov/pubmed/37037885 http://dx.doi.org/10.1038/s41598-023-33186-z |
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author | Tang, Li Yu, Xianyu Jiang, Weiwei Zhou, Jianguo |
author_facet | Tang, Li Yu, Xianyu Jiang, Weiwei Zhou, Jianguo |
author_sort | Tang, Li |
collection | PubMed |
description | The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM. |
format | Online Article Text |
id | pubmed-10086050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100860502023-04-12 Comparative study on landslide susceptibility mapping based on unbalanced sample ratio Tang, Li Yu, Xianyu Jiang, Weiwei Zhou, Jianguo Sci Rep Article The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM. Nature Publishing Group UK 2023-04-10 /pmc/articles/PMC10086050/ /pubmed/37037885 http://dx.doi.org/10.1038/s41598-023-33186-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tang, Li Yu, Xianyu Jiang, Weiwei Zhou, Jianguo Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title | Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title_full | Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title_fullStr | Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title_full_unstemmed | Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title_short | Comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
title_sort | comparative study on landslide susceptibility mapping based on unbalanced sample ratio |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086050/ https://www.ncbi.nlm.nih.gov/pubmed/37037885 http://dx.doi.org/10.1038/s41598-023-33186-z |
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