Forest Fire Prediction and Early Warning Assisted by Intelligent Systems

Forest fire prediction and early warning are important tasks to protect forest resources and the safety of human life and property. Traditional prediction methods have problems of low accuracy and slow processing speed, making it difficult to meet the realtime and accuracy requirements. This article aims to explore the application of intelligent systems in forest fire prediction and early warning, and compare the differences in prediction accuracy and realtime performance between intelligent systems and traditional methods through experiments. Experimental results show that the accuracy of the intelligent system can reach 96.875%, but the traditional method is faster in processing speed. Based on the experimental results, this article proposes countermeasures to improve realtime performance by optimizing algorithms and deployment architecture, and can combine the advantages of intelligent systems and traditional methods to achieve more comprehensive and efficient fire prediction and early warning. By improving the accuracy and realtime performance of intelligent systems, addressing data privacy and decision explainability challenges, and combining the advantages of intelligent systems and traditional methods, the effectiveness and efficiency of fire prediction and early warning can be improved. This has important practical significance for protecting forest resources and the safety of human life and property.