Interpretable rules for wildfire risk prediction using the binary golden ratio optimization method

Abstract

Forests are crucial for preserving biodiversity and regulating the global climate. However, they are increasingly at risk from destructive wildfires that threaten the environment and human communities. Accurate prediction models are essential to minimize the impact of forest fires. This study presents a new hybrid model that combines the Apriori association rule mining algorithm with the binary golden ratio optimization method (BGROM) to improve the accuracy of forest fire prediction. The BGROM, based on the golden ratio observed in plant and animal growth and formulated by the renowned mathematician Fibonacci. It is used to select the candidate features, which are then used by the Apriori algorithm to generate classification rules to predict the risk of wildfires. Integrating the Apriori algorithm with BGROM improves the accuracy of forest fire prediction and enhances our understanding of the complex interactions and patterns that influence wildfire behavior. This innovative approach holds great promise for advancing the development of effective forest fire prevention and management strategies. Experimental results show that the proposed model outperforms existing prediction methods, offering a more reliable tool for early forest fire detection and risk management.