English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Chimney fires constitute one of the most commonly occurring fire types.Precise prediction and prompt prevention are crucial in reducing the harm theycause. In this paper, we develop a combined machine learning and statisticalmodeling process to predict chimney fires. Firstly, we use random forests andpermutation importance techniques to identify the most informative explanatoryvariables. Secondly, we design a Poisson point process model and applyassociated logistic regression estimation to estimate the parameters. Moreover,we validate the Poisson model assumption using second-order summary statisticsand residuals. We implement the modeling process on data collected by theTwente Fire Brigade and obtain plausible predictions. Compared to similarstudies, our approach has two advantages: i) with random forests, we can selectexplanatory variables non-parametrically considering variable dependence; ii)using logistic regression estimation, we can fit the statistical modelefficiently by tuning it to focus on important regions and times of the firedata.

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