Spatial prediction of landslide susceptibility using random forest algorithm
Edited book (chapter)
Chapter Title | Spatial prediction of landslide susceptibility using random forest algorithm |
---|---|
Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 3337 |
Book Title | Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation |
Authors | Rahmati, Omid (Author), Kornejady, Aiding (Author) and Deo, Ravinesh C. (Author) |
Editors | Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher |
Page Range | 281-292 |
Series | Springer Transactions in Civil and Environmental Engineering |
Chapter Number | 15 |
Number of Pages | 12 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Singapore |
ISBN | 9789811557712 |
9789811557729 | |
ISSN | 2363-7633 |
2363-7641 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-15-5772-9_15 |
Web Address (URL) | https://link.springer.com/chapter/10.1007/978-981-15-5772-9_15 |
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. |
Keywords | landslide susceptibility mapping; random forest algorithm |
ANZSRC Field of Research 2020 | 410402. Environmental assessment and monitoring |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Kurdistan Agricultural and Natural Resources Research and Education Center, Iran |
Gorgan University of Agricultural Sciences and Natural Resources, Iran | |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5wqx/spatial-prediction-of-landslide-susceptibility-using-random-forest-algorithm
268
total views9
total downloads2
views this month0
downloads this month