Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

Abstract

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;

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

“…This algorithm is an extension of the well-known DBSCAN algorithm [50]. DBSCAN has been widely used to identify dense fire clusters [51][52][53][54][55]. Unlike DBSCAN, which relies on a single global density parameter ε (epsilon), HDBSCAN operates by performing clustering over varying ε values and integrating the results to identify the clustering that demonstrates the best stability across these values [56].…”