Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events

arXiv:2402.09553v1 Announce Type: cross
Abstract: Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We present methods for (i) data collection and dataset development; (ii) descriptive …

abstract arxiv city community consequences cs.ai economic emergency events families fire loss machine machine learning machine learning models paper prediction services statistical stat.ml type