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Bulletin of Engineering Geology and the Environment

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10.07.2020 | Original Paper

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

verfasst von: Sayed Naeim Emami, Saleh Yousefi, Hamid Reza Pourghasemi, Shahla Tavangar, M. Santosh

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 10/2020

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Abstract

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

Vorheriger Artikel In situ undrained shear strength for cutting slopes erodibility estimation: case study in the Quadrilátero Ferrífero region, Brazil

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Titel: A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)
verfasst von: Sayed Naeim Emami
Saleh Yousefi
Hamid Reza Pourghasemi
Shahla Tavangar
M. Santosh
Publikationsdatum: 10.07.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 10/2020
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-020-01915-7

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