Global and collective outliers detection on hotspot data as forest fires indicator in Riau Province, Indonesia

Forest fire in Indonesia is considered as an annual event that causes serious problems in health and environment especially in Sumatera and Kalimantan Islands. Studies on analyzing hotspot data as forest fire indicators are required for predicting hotspot occurrences. The objective of this work is to detect global and collective outliers on hotspot data in Riau Province in Sumatera Island for the period 2001-2012. The data used in this work are 4383 daily hotspots and 144 monthly hotspots. The method applied to discover outliers is the k-means clustering algorithm. The best clustering results are obtained on the number of clusters of 10 and the sum of squared error value is 18526.14. Based on the clustering results, we obtain 59 collective outliers and 30 global outliers on the hotspot dataset. The outliers on the hotspot data mostly occur in February, March, June, July, and August. The average frequency of outliers is 482.22 and the highest frequency of outliers is occurred in 2005. As many 1118 hotspots were found in the northern part of the Riau province on 21 June 2005. In August 2005 outliers spread on the whole area of Riau Province. For the period 2001-2012 there are no outliers occurred in April, November and December. This information is essential for an early warning system in forest fires prevention.