AI Applications In Forest Fire Prediction And Disaster Response

Abstract

This paper explores the applications of Artificial Intelligence (AI) in forest fire prediction and disaster response, focusing on the critical role AI plays in enhancing prediction accuracy, response efficiency, and disaster management. The objective is to review AI-driven techniques, including machine learning (ML) and deep learning (DL), used in the detection and forecasting of forest fires, and their integration into disaster response systems. A comprehensive examination of the data sources employed, such as satellite imagery, environmental sensors, and meteorological data, is presented. Key methodologies, such as supervised and unsupervised learning algorithms, predictive models, and AI-based decision support systems, are analyzed. Additionally, real-world applications and case studies where AI has been successfully utilized in fire prediction and response are discussed. The findings indicate that AI techniques, particularly deep learning models, can significantly improve the accuracy of fire prediction, enable real-time disaster monitoring, and enhance resource allocation during firefighting operations. However, challenges related to data quality, real-time processing, and integration with existing systems remain. The paper concludes by suggesting future research directions to improve AI models and their practical deployment in forest fire management.