Spatial prediction of landslide susceptibility using random forest algorithm

Rahmati, Omid and Kornejady, Aiding and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 (2021) Spatial prediction of landslide susceptibility using random forest algorithm. In: Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore, pp. 281-292. ISBN 978-981-15-5771-2


Abstract

Intelligent data analytics approaches are popular in landslide susceptibility mapping. This chapter develops a random forest (RF) approach for spatial modeling of landslide susceptibility. A total number of 78 landslide locations are identified using field survey, 55 of which are randomly selected to model landslide susceptibility and remaining 23 locations considered for model validation. Twelve predictor variables are selected: elevation, slope percentage, slope aspect, plan curvature, profile curvature, distance from roads, distance from streams, distance from faults, lithological formations, land use, soil type, and topographic wetness index (TWI) to create an RF model for landslide susceptibility mapping. The results of RF model are evaluated using efficiency (E), true positive rate (TPR), false positive rate (FPR), true skill statistic (TSS), and area under receiver operating characteristic curve (AUC) in training and validation steps. RF model registered excellent goodness-of-fit with AUC = 93.6%, E = 0.887, TSS = 0.776, TPR = 0.905, FPR = 0.129, and predictive performance with AUC = 90.7%, E = 0.777, TSS = 0.559, TPR = 0.809, FPR = 0.25. Intelligent data analytic method, therefore, has a significant promise in tackling challenges of landslide susceptibility mapping in large regions, which may not have sufficient geotechnical data to employ a physically based method.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published chapter in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 02 Sep 2020 02:20
Last Modified: 02 Sep 2020 02:20
Uncontrolled Keywords: landslide susceptibility mapping; random forest algorithm
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1007/978-981-15-5772-9_15
URI: http://eprints.usq.edu.au/id/eprint/39216

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