Abstract
Forest fires occur every year in Indonesia, one of the regions with the highest forest fires is Jambi Province. Significant losses and negative impacts due to forest fires cause the need for an effort to prevent forest fires early on with the detection of forest and land fires. One method that can provide information about the level of forest fire based on daily weather data input is the Fire Weather Index (Fire Weather Index / FWI) system, which was first developed by Canada. This study aims to estimate burn area in the Jambi region by using temperature, rainfall, humidity, and wind speed data. Other supporting data are hotspot frequency data from NASA-FIRMS satellites and data fraction of burn area from GFED satellites on a daily scale of the period 2006-2016. In this study an analysis of the relationship between these data variables and burn area estimation was carried out using multiple linear regression methods then validated to see the level of suitability of the output model forecasts. The results showed that the predictor variables that had the highest relationship were hotspots frequency, Buildup Index (BUI), and FWI index with correlation values of 0.888, 0.739 and 0.753, respectively. The estimation model of the resulting burnt area is: Burned Area = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × Hotspots) + 116, with an RMSE value of 635.524 and MAE of 491.38
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