Machine Learning Techniques in Structural Fire Risk Prediction

Fires often occur, and the damage caused by them is often irreversible.Fire-prone environments can be identified through historical data, and predictive models are recommended to prevent fires in advance.This study uses a variety of machine learning techniques to build fire prediction models and perform a comparative analysis to predict fires.We use data from local fire departments in South Korea to build fire prediction models using decision trees, random forest, XGBoost, extra tree classification, artificial neural networks, and more.Before creating the fire prediction models, we analyze and significant predictive features of a structural fire.We compared the fire prediction models and showed accuracy, F1-score, precision, and recall.The prediction model built with the random forest is the most accurate, but there is a little difference in the accuracy of each model trained with the extra tree classifier, XGBoost, and neural network.For the F1 Score, the model with a neural network shows the best value.