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Article

Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

1
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Geography, University of Connecticut, Storrs, CT 06269, USA
4
State Key Laboratory of Geohazard Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Bignami
Remote Sens. 2021, 13(6), 1157; https://doi.org/10.3390/rs13061157
Received: 20 February 2021 / Revised: 12 March 2021 / Accepted: 15 March 2021 / Published: 18 March 2021
Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping. View Full-Text
Keywords: Geo-detector; Random Forest; feature selection; landslide susceptibility mapping Geo-detector; Random Forest; feature selection; landslide susceptibility mapping
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MDPI and ACS Style

Liu, Y.; Zhang, W.; Zhang, Z.; Xu, Q.; Li, W. Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake. Remote Sens. 2021, 13, 1157. https://doi.org/10.3390/rs13061157

AMA Style

Liu Y, Zhang W, Zhang Z, Xu Q, Li W. Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake. Remote Sensing. 2021; 13(6):1157. https://doi.org/10.3390/rs13061157

Chicago/Turabian Style

Liu, Yimo, Wanchang Zhang, Zhijie Zhang, Qiang Xu, and Weile Li. 2021. "Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake" Remote Sensing 13, no. 6: 1157. https://doi.org/10.3390/rs13061157

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