Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models-Reference-Cited by-同舟云学术

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models

Published:2024-04-09 Issue:20 Volume:31 Page:29811-29835
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Mallick Javed^ORCID,Alkahtani Meshel,Hang Hoang Thi,Singh Chander Kumar

Funder

Deanship of Scientific Research, King Khalid University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-33128-w.pdf

Reference60 articles.

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2. Alqadhi S, Mallick J, Talukdar S, Ahmed M, Khan RA, Sarkar SK, Rahman A (2022c) Assessing the effect of future landslide on ecosystem services in Aqabat Al-Sulbat region, Saudi Arabia. Nat Hazards 113(1):641–671

3. Alqadhi S, Mallick J, Talukdar S, Bindajam AA, Saha TK, Ahmed M, Khan RA (2022a) Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping. Geocarto Int 37(25):9518–9543

4. Alqadhi S, Mallick J, Talukdar S, Bindajam AA, Van Hong N, Saha TK (2022b) Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environ Sci Pollut Res 29(3):3743–3762

5. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36

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