An overview of machine learning (ML) techniques applied to forest fire studies

Abstract

With the increasing frequency of forest fires globally, causing substantial environmental and economic damages, there is an imperative need for early fire prediction and detection. This study aims to examine the utility of machine learning techniques in predicting and identifying forest fires. A comprehensive review was conducted on various technologies and techniques proposed for forest fire prediction. Particular emphasis was placed on understanding the pros and cons of each machine learning algorithm, with an aim to identify the most effective approaches. It was observed that while numerous machine learning methods exist for forecasting forest fires, each possesses unique strengths and limitations. Some techniques, when tailored to specific forest characteristics, displayed enhanced predictive capabilities. Machine learning (ML) plays a pivotal role in advancing the field of forest fire studies. Identifying and utilizing the most suited ML technique, based on forest characteristics and the nature of data, can significantly augment prediction accuracy.