Long Short-Term Memory for Predicting Firemen Interventions

• Autor(a) principal: Nahuis, Selene Leya Cerna [UNESP]
• Data de Publicação: 2019
• Outros Autores: Guyeux, Christophe, Arcolezi, Heber Hwang [UNESP], Couturier, Raphael, Royer, Guillaume, Lotufo, Anna Diva Plasencia [UNESP], IEEE
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/196971

Many 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.