Incremental learning-random forest model-based landslide susceptibility analysis: A case of Ganzhou City, China-Reference-Cited by-同舟云学术

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Incremental learning-random forest model-based landslide susceptibility analysis: A case of Ganzhou City, China

Published:2024-02-21 Issue:2 Volume:17 Page:1645-1661
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Wang Xu,Nie Wen,Xie Wei,Zhang Yang

Funder

Natural Science Foundation of Jiangxi Province

Science and Technology Service Network Initiative

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12145-024-01229-2.pdf

Reference63 articles.

1. Abraham MT, Satyam N, Lokesh R et al (2021) Factors affecting landslide susceptibility mapping: Assessing the influence of different machine learning approaches, sampling strategies and data splitting[J]. Land 10(9):989

2. Akinci H, Zeybek M (2021) Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin). Turkey Natural Hazards 108(2):1515–1543

3. Ali R, Kuriqi A, Kisi O (2020) Human–environment natural disasters interconnection in China: a review[J]. Climate 8(4):48

4. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan[J]. Geomorphology 65(1–2):15–31

5. Berti M, Corsini A, Daehne A (2013) Comparative analysis of surface roughness algorithms for the identification of active landslides[J]. Geomorphology 182:1–18

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