Chimney fire prediction based on environmental variables

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

Chimney fire prediction based on environmental variables

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

^*Corresponding author for this work

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Abstract

Fire prediction and prevention is closely related to public security. In this paper, we propose a chimney fire prediction model using data collected by the Twente Fire Brigade. Random forests are used to select explanatory variables from a large set of candidates. Furthermore, we build a nested Poisson generalized linear model based on house types and weather variables. Plausible predictions are obtained, that capture the salient spatial and temporal patterns seen in the data.

Original language	English
Pages	288-291
Number of pages	4
Publication status	Published - 2021
Event	63rd World Statistics Congress, ISI 2021 - Virtual Duration: 11 Jul 2021 → 16 Jul 2021 Conference number: 63

Conference

Conference	63rd World Statistics Congress, ISI 2021
Abbreviated title	ISI 2021
City	Virtual
Period	11/07/21 → 16/07/21

Keywords

Areal unit data
Conditional variable importance
Poisson generalized linear model

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https://www.isi-web.org/files/docs/papers-and-abstracts/86-day2-cps036-chimney-fire-prediction-based.pdfLicence: CC BY

Cite this

@conference{dded0759d32040958127a3eef698b795,

title = "Chimney fire prediction based on environmental variables",

abstract = "Fire prediction and prevention is closely related to public security. In this paper, we propose a chimney fire prediction model using data collected by the Twente Fire Brigade. Random forests are used to select explanatory variables from a large set of candidates. Furthermore, we build a nested Poisson generalized linear model based on house types and weather variables. Plausible predictions are obtained, that capture the salient spatial and temporal patterns seen in the data.",

keywords = "Areal unit data, Conditional variable importance, Poisson generalized linear model",

author = "Changqing Lu and \{van Lieshout\}, Marie-Colette and \{de Graaf\}, Maurits and Paul Visscher",

year = "2021",

language = "English",

pages = "288--291",

note = "63rd World Statistics Congress, ISI 2021, ISI 2021 ; Conference date: 11-07-2021 Through 16-07-2021",

}

TY - CONF

T1 - Chimney fire prediction based on environmental variables

AU - Lu, Changqing

AU - van Lieshout, Marie-Colette

AU - de Graaf, Maurits

AU - Visscher, Paul

N1 - Conference code: 63

PY - 2021

Y1 - 2021

N2 - Fire prediction and prevention is closely related to public security. In this paper, we propose a chimney fire prediction model using data collected by the Twente Fire Brigade. Random forests are used to select explanatory variables from a large set of candidates. Furthermore, we build a nested Poisson generalized linear model based on house types and weather variables. Plausible predictions are obtained, that capture the salient spatial and temporal patterns seen in the data.

AB - Fire prediction and prevention is closely related to public security. In this paper, we propose a chimney fire prediction model using data collected by the Twente Fire Brigade. Random forests are used to select explanatory variables from a large set of candidates. Furthermore, we build a nested Poisson generalized linear model based on house types and weather variables. Plausible predictions are obtained, that capture the salient spatial and temporal patterns seen in the data.

KW - Areal unit data

KW - Conditional variable importance

KW - Poisson generalized linear model

M3 - Paper

SP - 288

EP - 291

T2 - 63rd World Statistics Congress, ISI 2021

Y2 - 11 July 2021 through 16 July 2021

ER -

Chimney fire prediction based on environmental variables

Abstract

Conference

Keywords

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Cite this