Imam Abdul Gani Alfarizi, Bagus Setiabudi Wiwoho, Hetty Rahmawati Sucahyo, Ike Sari Astuti
The increasing incidence of forest fires in Raden Soerjo's Grand Park over the last 5 years has become a concern for conducting research on fire predictions. The availability of free remote sensing data makes it easier to analyze forest fires using Geographic Information Systems (GIS). and remote sensing. Varying publicly available spatial datasets were used to classify forest fires affected areas with an approach of Machine Learning (ML) using Random Forest (RF). Nine (9) variables suspected as potential cause of forest fires (NDVI. ET. LST. Aspect. Slope. Altitude. Distance to Road (ED_Road). Distance to Built-up area (ED_BUA). and Distance to river (ED_River) were used as determinants. About 80% of the data were used as Trained data and 20% as a validation. The results of the model produced an accuracy of 0.96. The model result was checked for its sensitivity with the Area Under the Curve (AUC) AUC results with a value of 0.89. Findings show that Random Forest could be applied to map forest fire severity classes with a good result. © Published under licence by IOP Publishing Ltd.
Dept Of. Geography, State University of Malang, Malang, 65145, Indonesia; School of Earth and Environmental Sciences, The University of Queensland, 4067, QLD, Australia
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