Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China-Reference-Cited by-同舟云学术

Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China

Published:2019-10-03 Issue:1 Volume:100 Page:173-204
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Zhu Chonghao,Zhang Jianjing^ORCID,Liu Yang,Ma Donghua,Li Mengfang,Xiang Bo

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

http://link.springer.com/content/pdf/10.1007/s11069-019-03806-x.pdf

Reference51 articles.

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2. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34. https://doi.org/10.1016/j.cageo.2011.04.012

3. Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385. https://doi.org/10.1016/S0098-3004(97)00117-9

4. Aydln M, Karakuzu C, Ucar M et al (2013) Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning. Int J Adv Manuf Technol 67(1/4):957–967. https://doi.org/10.1007/s00170-012-4540-2

5. Baocheng M (2011) Research on identification technology of highway flood damage. Changan University, Xi’an

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