Data-driven chimney fire risk prediction using machine learning and point process tools

Chimney fires constitute one of the most commonly occurring fire types. Precise prediction and prompt prevention are crucial in reducing the harm they cause. In this paper we develop a combined machine learning and statistical modelling process to predict fire risk. First, we use random forests and permutation importance techniques to identify the most informative explanatory variables. Second, we design a Poisson point process model and employ logistic regression estimation to estimate the parameters. Moreover, we validate the Poisson model assumption using second-order summary statistics and residuals. We implement the modelling process on data collected by the Twente Fire Brigade and obtain plausible predictions. Compared to similar studies, our approach has two advantages: (i) with random forests, we can select explanatory variables nonparametrically considering variable dependence; (ii) using logistic regression estimation, we can fit our statistical model efficiently by tuning it to focus on regions and times that are salient for fire risk.

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