A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
(2022) In Remote Sensing 14(1).- Abstract
Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine... (More)
Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.
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- author
- Zhao, Pengxiang LU ; Masoumi, Zohreh ; Kalantari, Maryam ; Aflaki, Mahtab and Mansourian, Ali LU
- organization
- publishing date
- 2022-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Deep learning, Feature importance, Landslide causative factors, Landslide susceptibility mapping, Machine learning, Artificial intelligence (AI), Geospatial Artificial Intelligence (GeoAI)
- in
- Remote Sensing
- volume
- 14
- issue
- 1
- article number
- 211
- publisher
- MDPI AG
- external identifiers
-
- scopus:85122203111
- ISSN
- 2072-4292
- DOI
- 10.3390/rs14010211
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- f5f561fc-51da-4c5b-88fa-4489f5e23226
- date added to LUP
- 2022-01-26 20:40:45
- date last changed
- 2023-10-11 09:30:00
@article{f5f561fc-51da-4c5b-88fa-4489f5e23226,
abstract = {{<p>Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.</p>}},
author = {{Zhao, Pengxiang and Masoumi, Zohreh and Kalantari, Maryam and Aflaki, Mahtab and Mansourian, Ali}},
issn = {{2072-4292}},
keywords = {{Deep learning; Feature importance; Landslide causative factors; Landslide susceptibility mapping; Machine learning; Artificial intelligence (AI); Geospatial Artificial Intelligence (GeoAI)}},
language = {{eng}},
month = {{01}},
number = {{1}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods}},
url = {{http://dx.doi.org/10.3390/rs14010211}},
doi = {{10.3390/rs14010211}},
volume = {{14}},
year = {{2022}},
}