Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale-Reference-Cited by-同舟云学术

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Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale

Published:2022-08-06 Issue:3 Volume:114 Page:2709-2738
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Qin Shengwu^ORCID,Qiao Shuangshuang,Yao Jingyu,Zhang Lingshuai,Liu Xiaowei,Guo Xu,Chen Yang,Sun Jingbo

Funder

National Natural Science Foundation of China

Innovative Research Group Project of the National Natural Science Foundation of China

Department of Science and Technology of Jilin Province

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

https://link.springer.com/content/pdf/10.1007/s11069-022-05487-5.pdf

Reference100 articles.

1. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006

2. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003

3. Althuwaynee OF, Pradhan B, Park H, 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. https://doi.org/10.1016/j.catena.2013.10.011

4. Binh TP, Prakash I, Singh SK, Shirzadi A, Bui DT (2019) Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175:203–218. https://doi.org/10.1016/j.catena.2018.12.018

5. Blais-Stevens A, Behnia P (2016) Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska highway corridor, Canada. Nat Haz Earth Sys Sci 16(2):449–462. https://doi.org/10.5194/nhess-16-449-2016

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2. Comparison of debris flow susceptibility assessment methods: support vector machine, particle swarm optimization, and feature selection techniques;Journal of Mountain Science;2024-02

3. Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization;Natural Hazards;2023-08-07

4. Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model – taking Nujiang Prefecture, China as an example;International Journal of Digital Earth;2022-11-14

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