Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (5): 609-615.DOI: 10.12068/j.issn.1005-3026.2020.05.001

• Resources & Civil Engineering •     Next Articles

LandslidesSusceptibilityAssessmentBasedonDeepBeliefNetwork

WANG Wei-dong1,2, HE Zhuo-lei1, HAN Zheng1, QIAN Yu3   

  1. 1.School of Civil Engineering,Central South University, Changsha 410075, China; 2.The Key Laboratory of Engineering Structures of Heavy Haul Railway, Ministry of Education, Central South University, Changsha 410075, China; 3.Department of Civil and Environmental Engineering, The University of South Carolina, Columbia 29208, America.
  • Received:2019-08-26 Revised:2019-08-26 Online:2020-05-15 Published:2020-05-15
  • Contact: HAN Zheng
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Abstract: Complex non-linear relationships exist among causing factors in landslides susceptibility assessment. Traditional assessment models is difficult to reveal such complex relationships, and limit the accuracy of assessment results. Based on literature review and field survey, the altitude, landform, lithology, slope, distance to tectonic line, distance to drainage network and annual average rainfall were chosen as the main causing factors. A regional landslides susceptibility mapping model based on deep belief network (DBN) model in geographic information system (GIS) was established, and Sichuan Province was taken as an example. Finally, through the characteristics of ROC curves, the assessment results were compared with logistic regression (LR) and BP neural network (BPNN), and the response of each model to different causing factors was discussed. The results show that the DBN model has high accuracy, low false positive rate and false negative rate, and is suitable for landslides susceptibility assessment in large area with complex causing factors.

Key words: geographic information system(GIS), landslides susceptibility assessment, deep learning, deep belief network(DBN), ROC curve

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