Work

Development and Verification of a Fire Occurrence Prediction Model with Social-Architectural Factors

Year: 2024

Type: article

Abstract: In this study, we aim to develop a fire occurrence prediction model at the administrative district level by incorporating social-architectural factors. Based on data on social and architectural factors and the number of fire occurrences from 2015~2021 in various cities, counties, and districts, multiple machine learning algorithms (multilayer perceptron, LASSO regression, and random forest) were employed to implement and compare the performance of the fire occurrence prediction models. The results indicated that the model utilizing the random forest algorithm exhibited the best prediction performance. Furthermore, performance validation using 2022 data showed that out of 247 administrative districts, 171 had an error rate of 20% or less.

Source: Fire science and engineering

DOI: 10.7731/kifse.de3a30b9

Authors: Chu Zhang, Won-Hwa Hong, Young-Hoon Bae

Language: English

Topic: Fire dynamics and safety research

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

Funders: Ministry of Education, National Research Foundation of Korea

Award: G12186911