Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

Muchamad Taufiq Anwar (Universitas Stikubank Semarang)
Wiwien Hadikurniawati (Universitas Stikubank Semarang)
Edy Winarno (Universitas Stikubank Semarang)
Aji Supriyanto (Universitas Stikubank Semarang)

Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 1, No 1 (2019)

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;