Secondary-factor division optimization based on ET-GA to improve the accuracy of LSA obtained using SSA-DBN compared to the original division method: a case study in southern Sichuan, China-Reference-Cited by-同舟云学术

Secondary-factor division optimization based on ET-GA to improve the accuracy of LSA obtained using SSA-DBN compared to the original division method: a case study in southern Sichuan, China

Published:2023-07-21 Issue:8 Volume:82 Page:
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Li Jiaying,Wang Weidong,Chen Guangqi,Han Zheng

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10064-023-03331-z.pdf

Reference46 articles.

1. Arora N, Kaur PD (2020) A Bolasso based consistent feature selection enabled random forest classification algorithm: an application to credit risk assessment. Appl Soft Comput 86:105936. https://doi.org/10.1016/j.asoc.2019.105936

2. Balogun AL, Rezaie F, Pham QB, Gigović L, Drobnjak S, Aina YA, Panahi M, Yekeen ST, Lee S (2021) Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geosci Front 12:101104. https://doi.org/10.1016/j.gsf.2020.10.009

3. Bragagnolo L, da Silva RV, Grzybowski JMV (2020a) Landslide susceptibility mapping with r.landslide: a free open-source GIS-integrated tool based on Artificial Neural Networks. Environ Model Softw 123:104565. https://doi.org/10.1016/j.envsoft.2019.104565

4. Bragagnolo L, Silva RVD, Grzybowski JMV (2020b) Artificial neural network ensembles applied to the mapping of landslide susceptibility. CATENA 184:104240. https://doi.org/10.1016/j.catena.2019.104240

5. Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436. https://doi.org/10.1007/s10346-014-0550-5