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ThinkMind // DATA ANALYTICS 2019, The Eighth International Conference on Data Analytics // View article data_analytics_2019_4_10_68002


Applied Urban Fire Department Incident Forecasting

Authors:
Guido Legemaate
Jeffrey de Deijn
Sandjai Bhulai
Rob van der Mei

Keywords: incident forecasting, fire department planning, generalized linear models, ensemble models

Abstract:
Every day, when firefighters respond to emergencies, they and the public face an unnecessary risk due to inadequate staffing. Having too many people stand-by costs a lot of money, on the other hand, having too few people stand-by leads to unnecessary safety risks. Therefore, for adequate staffing purposes, forecasting the number of incidents that each fire station has to handle is a very relevant question. In this paper, we develop models to create a good forecast for the number of incidents that each fire station in Amsterdam-Amstelland has to handle. Previous studies mainly focused on multiplicative models containing correction factors for the weekday and the time of the year. Our main contribution is to incorporate the influence of different weather conditions in the categories of wind, temperature, rain, and visibility. We show that an ensemble model has the best predictive performance. Rain and wind typically have a strong linear influence, while temperature mainly has a non-linear influence.

Pages: 57 to 62

Copyright: Copyright (c) IARIA, 2019

Publication date: September 22, 2019

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-741-2

Location: Porto, Portugal

Dates: from September 22, 2019 to September 26, 2019

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