Advancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine Learning

Abstract

This research conducts a comprehensive investigation of the efficacy of various machine learning algorithms for fire detection. The algorithms that were examined include logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron classifier, and XGBoost classifier. Through in-depth experiments, this study rigorously assesses the performance of these algorithms in identifying and predicting fires based on pertinent input features. Among the algorithms that were investigated, logistic regression is the best performer, with a high accuracy rate of 99%. The findings from this research offer valuable insights for optimizing fire detection systems, providing a nuanced understanding of the practical applicability of machine learning techniques in real-time fire monitoring scenarios. The primary objectives of this study are to elucidate specific challenges in fire detection, evaluate the performance of various machine learning algorithms, and contribute to the foundational knowledge that is essential for enhancing fire management strategies. The research addresses the limited precision of existing fire detection systems and aims to rectify this issue through a systematic exploration of advanced machine learning approaches. The overarching goal is to bolster the foundations of fire management, facilitating the development of proactive measures and prompt responses to mitigate the profound impact of wildfires. By presenting a detailed examination of the strengths and weaknesses of various machine learning algorithms, this research strives to foster a robust and effective approach to fire detection, thereby advancing the field and ensuring the safety of communities at risk.

Keywords

• decisiontreeclassifier , early warning systems, classifier, and xgboost, fire detection