Deep learning ensembles for accurate fog-related low-visibility events forecasting
Authors
Peláez Rodríguez, César; Pérez Aracil, Jorge; López Diz, Alba de; Casanova Mateo, C.; Fister, Dusan; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60853DOI: 10.1016/j.neucom.2023.126435
ISSN: 0925-2312
Publisher
Elsevier
Date
2023-09-07Funders
Agencia Estatal de Investigación
Bibliographic citation
Peláez Rodríguez, C. [et al.], 2023, "Deep learning ensembles for accurate fog-related low-visibility events forecasting", Neurocomputing, vol. 549, art. no. 126435, pp. 1-26.
Keywords
Deep learning ensembles
Machine learning
Low-visibility events
Forecasting
Orographic fog
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115454GB-C21/ES/NUEVOS ALGORITMOS NEURO-EVOLUTIVOS PARA CLASIFICACION ORDINAL: APLICACIONES EN CLIMA, ENERGIAS LIMPIAS Y MEDIO AMBIENTE/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1016/j.neucom.2023.126435Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2023 The authors
Access rights
info:eu-repo/semantics/openAccess
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 mod- els 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.
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