A novel information changing rate and conditional mutual information-based input feature selection method for artificial intelligence drought prediction models-Reference-Cited by-同舟云学术

A novel information changing rate and conditional mutual information-based input feature selection method for artificial intelligence drought prediction models

Published:2022-01-31 Issue:11-12 Volume:58 Page:3405-3425
ISSN:0930-7575
Container-title:Climate Dynamics
language:en
Short-container-title:Clim Dyn

Author:

Li Qiongfang,Han Xingye^ORCID,Liu Zhennan,He Pengfei,Shi Peng,Chen Qihui,Du Furan

Funder

the National Natural Science Foundation Commission of China

the Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water ecological civilization, China

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00382-021-06104-0.pdf

Reference126 articles.

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3. Aghelpour P, Bahrami-Pichaghchi H, Kisi O (2020) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comput Electron Agric. https://doi.org/10.1016/j.compag.2020.105279

4. Ali M, Deo RC, Downs NJ, Maraseni T (2018a) Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting. Comput Electron Agric. https://doi.org/10.1016/j.compag.2018.07.013

5. Ali M, Deo RC, Downs NJ, Maraseni T (2018b) An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index. Atmos Res. https://doi.org/10.1016/j.atmosres.2018.02.024

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