A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

Author:

Fanos Ali Mutar,Pradhan BiswajeetORCID,Mansor Shattri,Yusoff Zainuddin Md,Abdullah Ahmad Fikri bin

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology

Reference57 articles.

1. Abdulwahid WM, Pradhan B (2017) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14(3):1057–1076. https://doi.org/10.1007/s10346-016-0744-0

2. Acosta E, Agliardi F, Crosta G B, Rıos Aragues S (2007) Regional rockfall hazard assessment in the Benasque Valley (Central Pyrenees) using a 3D numerical approach. In 4th EGS Plinius Conference–Mediterranean Storms, pp. 555–563

3. Agliardi F, Riva F, Galletti L, Zanchi A Crosta G B (2016) Rockfall source characterization at high rock walls in complex geological settings by photogrammetry, structural analysis and DFN techniques. In EGU General Assembly Conference Abstracts 18, p. 13071

4. Aksoy H, Ercanoglu M (2006) Determination of the rockfall source in an urban settlement area by using a rule-based fuzzy evaluation. Nat Hazard Earth Syst 6(6):941–954

5. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36

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