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.
A forest fire early warning system must be developed to reduce the impact of greater community losses. One effort to develop an early warning system is to use a forest fire hazard index as a potential assessment guide. The main factor which is a parameter in the fire hazard index calculation method is the meteorological parameter. In general, to know today’s fire hazard index is calculated from today’s weather conditions, but the need for an early warning system is to know the future fire hazard index. Based on a series of meteorological conditions data held for thirty-six months, using the backpropagation algorithm, it is estimated that the meteorological conditions will be several months to come. Several meteorological parameters have their respective roles, the unknown contribution of which is calculated. In this study, each parameter will be measured by predicting time series data and compared with the results of calculations. The method of calculating the forest fire index used is the McArthur Forest Fire Danger Index with the meteorological parameter elements are temperature, relative humidity, wind speed, and drought factor. Each parameter was trained in artificial neural networks and tested its predictions to produce accuracy for data series temperatures of 91.67%, the relative humidity of 83.33%, and wind speed of 50%.
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