Abstract
Being able to predict the daily activity of firefighters is of great interest to optimize human and material resources. It will allow to enable a quicker response by achieving a better geographical deployment of these resources according to the expected number of interventions. Having obtained the list of interventions for the period 2012–2017 in the Department of the Doubs, France, we added a relevant collection of explanatory variables based on calendar data (time of day, day of the week, day of the month, year, public holidays, etc.), road traffic, meteorological and astronomical data, and so on. After detecting outliers and completing missing data, this set has been divided for learning, validating, and testing. The learning is then carried out on an ad hoc multilayer perceptron whose hyperparameters are finely defined using some supercomputer facilities. This neural architecture are finally applied on a real case study, that is, to the predictions of firemen interventions for the year 2017 after a learning stage on 2012–2016, leading to really encouraging results.
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Acknowledgments
This study has been supported by the EIPHI Graduate School (contract “ANR-17-EURE-0002”), by the Interreg RESponSE project, and by the SDIS25 firemen brigade. All computations have been performed on the supercomputer facilities of the Mésocentre de Calculs de l’Université de Franche-Comté.
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Guyeux, C. et al. (2020). Firemen Prediction by Using Neural Networks: A Real Case Study. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_42
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DOI: https://doi.org/10.1007/978-3-030-29516-5_42
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