Long short-term memory for predicting firemen interventions

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dc.contributor.authorNahuis, Selene Leya Cerna [UNESP]
dc.contributor.authorGuyeux, Christophe
dc.contributor.authorArcolezi, Heber Hwang [UNESP]
dc.contributor.authorCouturier, Raphael
dc.contributor.authorRoyer, Guillaume
dc.contributor.authorLotufo, Anna Diva Plasencia [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionCNRS
dc.contributor.institutionSDIS 25 - Service Départemental d'Incendie et de Secours du Doubs
dc.date.accessioned2020-12-12T01:40:21Z
dc.date.available2020-12-12T01:40:21Z
dc.date.issued2019-04-01
dc.description.abstractMany environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and they, therefore, face an ever-increasing number of interventions, most of the time with constant resources. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters with constant resources is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The purpose of this article is to show that these interventions can indeed be predicted, in a nonabsurd way, from state-of-the-art tools such as recurrent long short-term memory neural networks (LSTM). From the list of interventions in the Doubs (France), we show that it is possible to build, from scratch, a neural network capable of reasonably predicting the interventions of 2017 from those of 2012-2016. While the results could be improved, they are already promising and would allow the actions of firefighters with a constant resource to be optimized.en
dc.description.affiliationDepartment of Electrical Engineering São Paulo State University UNESP Ilha Solteira
dc.description.affiliationFEMTO-ST Institute Univ. Bourgogne Franche-Comte (UBFC) CNRS
dc.description.affiliationSDIS 25 - Service Départemental d'Incendie et de Secours du Doubs
dc.description.affiliationUnespDepartment of Electrical Engineering São Paulo State University UNESP Ilha Solteira
dc.format.extent1132-1137
dc.identifierhttp://dx.doi.org/10.1109/CoDIT.2019.8820671
dc.identifier.citation2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, p. 1132-1137.
dc.identifier.doi10.1109/CoDIT.2019.8820671
dc.identifier.scopus2-s2.0-85072845047
dc.identifier.urihttp://hdl.handle.net/11449/199457
dc.language.isoeng
dc.relation.ispartof2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019
dc.sourceScopus
dc.titleLong short-term memory for predicting firemen interventionsen
dc.typeTrabalho apresentado em evento

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Artigos - Engenharia Elétrica - FEIS