Combination of Bagging and Neural Network for Improving Precipitation Estimates Using Remote Sensing Data

Author:

Lazri Mourad,Attaf Youcef,Labadi Karim,Ouallouche Fethi,Hameg Slimane,Ameur Soltane

Publisher

Springer Nature Singapore

Reference24 articles.

1. Belghit A, Lazri M, Ouallouche F, Labadi K, Ameur S (2023) Optimization of one versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data. Adv Space Res 71:946. https://doi.org/10.1016/j.asr.2022.08.075

2. Belmahdi F, Lazri M, Ouallouche F, Labadi K, Absi R, Ameur S (2023) Application of Dempster-Shafer theory for optimization of precipitation classification and estimation results from remote sensing data using machine learning. Remote Sens Appl: Soc Environ 29:100906

3. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

4. EUMETSAT (2004) Applications of Meteosat second generation – conversion from counts to radiances and from radiances to brightness temperatures and reflectance. http://oiswww.eumetsat.org/WEBOPS/msg_interpretation/index.html

5. González S, García S, Del Ser J, Rokach L, Herrera F (2020) A practical tutorial on bagging and boosting based ensembles for machine learning: algorithms, software tools, performance study, practical perspectives and opportunities. Inform Fusion 64:205–237

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