Hybridization of rough set–wrapper method with regularized combinational LSTM for seasonal air quality index prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09220-6.pdf
Reference42 articles.
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3. Zhang B, Zhang Y, Jiang X (2022) Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm. Sci Rep 12:9244. https://doi.org/10.1038/s41598-022-13498-2
4. Liu H, Chen C (2020) Spatial air quality index prediction model based on decomposition, adaptive boosting, and three-stage feature selection: A case study in China. J Cleaner Prod 265:121777. https://doi.org/10.1016/j.jclepro.2020.121777
5. Zhao Z, Wu J, Cai F, Zhang S, Wang YG (2022) A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting. Ecolog Indicat 144:109416. https://doi.org/10.1016/j.ecolind.2022.109416
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