Machine Learning for predicting firefighters' interventions per type of mission

Fire brigades' operations vary with time, climate, season, occasions, etc. For example, the frequency of accidents is greater during the day than at night. Thus, adjusting the need to the demand of fire departments by categories of operations can lead to a reduction of material, financial and human resources. Therefore, it can be very helpful during the financial and economic crisis most countries face. It also helps firefighters to be well prepared by knowing the type and number of human resources needed for the next operation. The aim of this study is to predict the number of firefighters' interventions of 14 different categories varying between emergency and non-emergency deployments. The experiments in this study on the dataset provided by the fire and rescue service, SDIS 25, in the Doubs-France region showed that it is not necessary to improve the prediction when more explanatory variables are added. Some characteristics are not informative and may reduce the accuracy of the results.