Abstract
Predicting the number of firemen interventions to size the appropriate workload of firefighters to the appropriate need is vital for reducing material and human resources. Therefore, it will have a great impact on reducing the financial crisis resulting from global warming and population growth. The database in this research includes interventions recorded hourly from “1 January, 2015 00:00:00” to “31 December, 2019 23:00:00” in Doubs, France. The data were processed, decomposed, outliers were detected and replaced. Thenceforth, optimal smoothing values were selected and then three different models of Exponential Smoothing were deployed. Experiments have shown that Holt-Winters’ method has the best accuracy comparing to the baseline and other Exponential Smoothing techniques. The results are promising and would optimize the number of firefighters’ resources.
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Acknowledgement
This work has been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002) and is partially funded with support from the Hubert Curien CEDRE programme n 46543ZD.
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Mallouhy, R.E., Guyeux, C., Jaoude, C.A., Makhoul, A. (2022). Forecasting the Number of Firemen Interventions Using Exponential Smoothing Methods: A Case Study. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_50
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