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

Changqing Lu*, Marie-Colette van Lieshout, Maurits de Graaf, Paul Visscher

*Corresponding author for this work

Research output: Working paperPreprint

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Abstract

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 modeling process to predict chimney fires. Firstly, we use random forests and permutation importance techniques to identify the most informative explanatory variables. Secondly, we design a Poisson point process model and apply associated logistic regression estimation to estimate the parameters. Moreover, we validate the Poisson model assumption using second-order summary statistics and residuals. We implement the modeling 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 non-parametrically considering variable dependence; ii) using logistic regression estimation, we can fit the statistical model efficiently by tuning it to focus on important regions and times of the fire data.
Original languageEnglish
PublisherarXiv.org
Publication statusSubmitted - 2021

Keywords

  • fire prediction
  • spatio-temporal point pattern
  • Poisson point process
  • variable importance
  • logistic regression
  • pair correlation function
  • K-function

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