Modelling Uncertainties and Sensitivity Analysis of Landslide Susceptibility Prediction under Different Environmental Factor Connection Methods and Machine Learning Models-Reference-Cited by-同舟云学术

Modelling Uncertainties and Sensitivity Analysis of Landslide Susceptibility Prediction under Different Environmental Factor Connection Methods and Machine Learning Models

Published:2023-12-01 Issue:1 Volume:28 Page:45-62
ISSN:1226-7988
Container-title:KSCE Journal of Civil Engineering
language:en
Short-container-title:KSCE J Civ Eng

Author:

Huang Faming^ORCID,Xiong Haowen^ORCID,Zhou Xiaoting^ORCID,Catani Filippo^ORCID,Huang Jinsong^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s12205-023-2430-9.pdf

Reference53 articles.

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2. Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers 11(3):871–883, DOI: https://doi.org/10.1016/j.gsf.2019.10.001

3. Adnan MSG, Rahman MS, Ahmed N, Ahmed B, Rabbi MF, Rahman RM (2020) Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sensing 12(20):3347, DOI: https://doi.org/10.3390/rs12203347

4. Aghdam IN, Pradhan B, Panahi M (2017) Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environmental Earth Sciences 76(6):237, DOI: https://doi.org/10.1007/s12665-017-6558-0

5. Akinci H, Yavuz Ozalp A (2021) Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophysica 69(3):725–745, DOI: https://doi.org/10.1007/s11600-021-00577-7

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