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• W4380201700 abstract "In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem." @default.
• W4380201700 created "2023-06-11" @default.
• W4380201700 creator A5024548236 @default.
• W4380201700 creator A5034384417 @default.
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• W4380201700 creator A5092135550 @default.
• W4380201700 date "2023-09-01" @default.
• W4380201700 modified "2023-10-18" @default.
• W4380201700 title "Deep learning ensembles for accurate fog-related low-visibility events forecasting" @default.
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• W4380201700 doi "https://doi.org/10.1016/j.neucom.2023.126435" @default.
• W4380201700 hasPublicationYear "2023" @default.
• W4380201700 type Work @default.
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