Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China-Reference-Cited by-同舟云学术

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Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China

Published:2024-08 Issue:8 Volume:16 Page:3221-3232
ISSN:1674-7755
Container-title:Journal of Rock Mechanics and Geotechnical Engineering
language:en
Short-container-title:Journal of Rock Mechanics and Geotechnical Engineering

Author:

Sun Deliang,Wang Jing,Wen Haijia^ORCID,Ding YueKai,Mi Changlin

Funder

Natural Science Foundation Project of Chongqing

Publisher

Elsevier BV

Reference66 articles.

1. Landslide susceptibility mapping using genetic algorithm for the rule set production (GARP) model;Adineh;J. Mt. Sci.,2018

2. A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset;Al-Najjar;Gondwana Res.,2022

3. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling;Alvioli;Geosci. Model Dev. (GMD),2016

4. Parameter-free delineation of slope units and terrain subdivision of Italy;Alvioli;Geomorphology,2020

5. Landslide susceptibility assessment of south pars special zone, southwest Iran;Azarafza;Environ. Earth Sci.,2018

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm;Earth Science Informatics;2024-08-31

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