Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas-Reference-Cited by-同舟云学术

Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas

Published:2023-08-02 Issue:12 Volume:37 Page:4641-4660
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Bien Tran Xuan,Iqbal Mudassir,Jamal Arshad,Nguyen Dam Duc,Van Phong Tran,Costache Romulus,Ho Lanh Si,Van Le Hiep,Nguyen Hanh Bich Thi,Prakash Indra,Pham Binh Thai

Funder

Vietnam National Foundation for Science and Technology Development

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-023-02521-1.pdf

Reference86 articles.

1. Abedini M, Ghasemian B, Shirzadi A, Bui DT (2019) A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling. Environ Earth Sci 78(18):1–15

2. Adnan MN, Islam MZ (2017) Forest PA: constructing a decision forest by penalizing attributes used in previous trees. Expert Syst Appl 89:389–403

3. Ado M, Amitab K, Maji AK, Jasińska E, Gono R, Leonowicz Z, Jasiński M (2022) Landslide susceptibility mapping using machine learning: a literature survey. Remote Sens 14(13):3029