A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study
Author:
Affiliation:
1. College of Construction Engineering, Jilin University, Changchun, Jilin, China
2. Engineering Department, Northeast Electric Power Design Institute Co., Ltd, Changchun, Jilin, China
Funder
the National Key Research and Development Program of China
the National Natural Science Foundation of China
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.1955018
Reference79 articles.
1. Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
2. GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms
3. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
4. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
5. Susceptibility to Environmental Impact in the Queen Elizabeth Islands
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