A generalized regression neural network–based method for predicting cumulative infiltration volume in shallow slopes-Reference-Cited by-同舟云学术

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A generalized regression neural network–based method for predicting cumulative infiltration volume in shallow slopes

Published:2021-07-23 Issue:9 Volume:80 Page:7269-7285
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Li S. H.,Liu G. G.,Zhang S. X.

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10064-021-02364-6.pdf

Reference58 articles.

1. Abedini M, Ghasemian B, Shirzadi A, Bui DT (2019) A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling. Environ Earth Sci 78(18):560

2. Barry DA, Parlange JY, Li L, Jeng DS, Crapper M (2005) Green-Ampt approximations. Adv Water Resour 28(10):1003–1009

3. Behzadafshar K, Sarafraz ME, Hasanipanah M, Mojtahedi SFF, Tahir MM (2019) Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results. Bull Eng Geol Env 78:1527–1536

4. Bevilacqua I, Canone D, Ferraris S (2009) Acceleration techniques for the iterative resolution of the Richards equation by the finite volume method. Int J Numer Methods Biomed Eng 27(8):1309–1320

5. Bordoni M, Galanti Y, Bartelletti C et al (2020) The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models. CATENA 193:104630

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