A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin County, China

Wei Xie, Xiaoshuang Li, Wenbin Jian, Yang Yang, Hongwei Liu, Luis F. Robledo, Wen Nie

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo93
PublicaciónISPRS International Journal of Geo-Information
Volumen10
N.º2
DOI
EstadoPublicada - feb 2021

Áreas temáticas de ASJC Scopus

  • Geografía, planificación y desarrollo
  • Informática en Ciencias de la Tierra
  • Ciencias planetarias y de la Tierra (miscelánea)

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