A Comparative Study of the Forest Fire Danger Index Calculation Methods Using Backpropagation

Abstract

Forest fires are one of the threats of disasters in Indonesia. Rising earth temperatures add to the higher potential contribution of forest fires. Many forest fire hazard index calculation methods have been developed to analyze the impact categories arising from changes in meteorological parameters. Each calculation method has advantages in calculating the magnitude of the potential. One method of calculating the forest fire hazard index is needed from many choices of methods that have the highest level of accuracy to predict the potential for a fire. A comparative study of the methods will be validated and guide the best calculating procedure to be implemented for the prediction. Predictions that have excellent accuracy and precision can be used as an early warning system. This study will predict forest fires for each calculation method using the Backpropagation algorithm, then analyze the accuracy of the prediction results using Relative Operating Characteristics (ROC). The methods compared include the methods that have been used in Indonesia as a country that has tropical rainforests, namely Keetch-Byram Drought Index (KBDI), Standard Precipitation Index (SPI), McArthur Forest Fire Danger Index (MFFDI), and Fire Weather Index (FWI). Through a comparative study of this calculation model, it is concluded that MFFDI is the best method of calculating the fire hazard index with an accuracy value of 0.917 and a precision value of 0.667.