A Comprehensive Machine Learning Approach for Accurate Forest Fire Forecasting

Abstract

Forest fires have significant environmental and economic impacts, necessitating advanced prediction and detection strategies. Artificial intelligence, particularly machine learning (ML) techniques, plays a crucial role in addressing this challenge. Supervised models analyze complex patterns, enabling early warnings and comprehensive risk evaluation. This chapter critically examines the suitability and limitations of various ML algorithms in forest fire science, emphasizing the need to carefully consider algorithmic strengths and weaknesses. It highlights challenges in selecting the most suitable models for accurate predictions and advocates for customizing ML models to specific forest characteristics. By tailoring ML techniques based on unique forest attributes, the research aims to enhance prediction accuracy, improve risk assessment, and implement targeted preventive measures. Ultimately, this contributes to a more proactive and effective approach to forest fire management.