A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression-Reference-Cited by-同舟云学术

A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression

Published:2022-02-12 Issue:10 Volume:36 Page:3109-3129
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Ma Junwei^ORCID,Wang Yankun,Niu Xiaoxu,Jiang Sheng,Liu Zhiyang

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-022-02183-5.pdf

Reference60 articles.

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3. Cao Y, Yin KL, Alexander DE, Zhou C (2016) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13:725–736. https://doi.org/10.1007/s10346-015-0596-z

4. Chen Y, Yuan H (2020) Evaluation of nine sub-daily soil moisture model products over China using high-resolution in situ observations. J Hydrol 588:125054. https://doi.org/10.1016/j.jhydrol.2020.125054

5. Darudi A, Rezaeifar S, Mohammd Hossein Javidi Dasht B (2013) Partial mutual information based algorithm for input variable selection For time series forecasting. 2013 13th International conference on environment and electrical engineering (EEEIC), 313–318.

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