Abstract
Ensemble Deep Learning Architectures have demonstrated to improve the performance in comparison with the individual architectures composing the ensemble. In the current work, an ensemble of variants of Convolutional and Recurrent Neural Networks architectures are applied to the prediction of the \(^{222}Rn\) level at the Canfranc Underground Laboratory (Spain). To predict the low-level periods allows appropriately scheduling the maintenance operations in the experiments hosted in the laboratory. As a consequence of the application of Ensemble Deep Learning, an improvement of the forecasting capacity is stated. Furthermore, the learned lessons from this work can be extrapolated to other underground laboratories around the world.
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Acknowledgment
The research leading to these results has received funding by the Spanish Ministry of Economy and Competitiveness (MINECO) for funding support through the grant FPA2016-80994-C2-1-R, and “Unidad de Excelencia María de Maeztu”: CIEMAT - FÍSICA DE PARTÍCULAS through the grant MDM-2015-0509.
IMJ is co-funded in a 91.89 percent by the European Social Fund within the Youth Employment Operating Program, for the programming period 2014–2020, as well as Youth Employment Initiative (IEJ). IMJ is also co-funded through the Grants for the Promotion of Youth Employment and Implantation of Youth Guarantee in Research and Development and Innovation (I+D+i) from the MINECO.
The authors would like to thank Roberto Santorelli, Pablo García Abia and Vicente Pesudo for useful comments regarding the Physics related aspects of this work, and the Underground Laboratory of Canfranc by providing valuable feedback.
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Cárdenas-Montes, M., Méndez-Jiménez, I. (2020). Ensemble Deep Learning for Forecasting \(^{222}Rn\) Radiation Level at Canfranc Underground Laboratory. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_15
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