Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks

Rui Xuan Tang, Pinnaduwa H.S.W. Kulatilake, E. Chuan Yan, Jing Sen Cai

Research output: Contribution to journalArticle

Abstract

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

Original languageEnglish (US)
JournalBulletin of Engineering Geology and the Environment
DOIs
StateAccepted/In press - Jan 1 2020

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Keywords

  • Artificial neural networks
  • China
  • Cluster analysis
  • GIS
  • Landslide susceptibility
  • Probabilistic method

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology

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