TY - JOUR
T1 - A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster
T2 - A case of Xiaojin County, China
AU - Xie, Wei
AU - Li, Xiaoshuang
AU - Jian, Wenbin
AU - Yang, Yang
AU - Liu, Hongwei
AU - Robledo, Luis F.
AU - Nie, Wen
N1 - Publisher Copyright:
© 2021 by the authors.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - Geodetector
KW - GIS
KW - Landslide susceptibility mapping
KW - Machine learning
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85105228203&partnerID=8YFLogxK
U2 - 10.3390/ijgi10020093
DO - 10.3390/ijgi10020093
M3 - Article
AN - SCOPUS:85105228203
SN - 2220-9964
VL - 10
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 2
M1 - 93
ER -