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Article

A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

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School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
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School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu 610500, China
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College of Civil Engineering and Architecture, Guangxi University of Science and Technology, Liuzhou 545006, China
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Department of Geotechnical and Geological Engineering, Fuzhou University, Fuzhou 350108, China
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Engineering Science Department, Universidad Andres Bello, Santiago 7500971, Chile
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Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, 362000, China
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Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Biswajeet Pradhan
ISPRS Int. J. Geo-Inf. 2021, 10(2), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020093
Received: 22 December 2020 / Revised: 30 January 2021 / Accepted: 16 February 2021 / Published: 20 February 2021
Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility. View Full-Text
Keywords: landslide susceptibility mapping; GeoDetector; machine learning; GIS; support vector machines landslide susceptibility mapping; GeoDetector; machine learning; GIS; support vector machines
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MDPI and ACS Style

Xie, W.; Li, X.; Jian, W.; Yang, Y.; Liu, H.; Robledo, L.F.; Nie, W. A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China. ISPRS Int. J. Geo-Inf. 2021, 10, 93. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020093

AMA Style

Xie W, Li X, Jian W, Yang Y, Liu H, Robledo LF, Nie W. A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China. ISPRS International Journal of Geo-Information. 2021; 10(2):93. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020093

Chicago/Turabian Style

Xie, Wei, Xiaoshuang Li, Wenbin Jian, Yang Yang, Hongwei Liu, Luis F. Robledo, and Wen Nie. 2021. "A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China" ISPRS International Journal of Geo-Information 10, no. 2: 93. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020093

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