Outlier Detection on Hotspots Data in Riau Province using DBSCAN Algorithm

Workshop and International Seminar on Science of Complex Natural Systems

Sukmasetya, Pristi^1 ; Sitanggang, Imas S.^1

Computer Science Department, Bogor Agricultural University, Kampus IPB, Jalan Meranti Wing 20, West Java, Bogor, Indonesia^1

关键词: DBSCAN algorithm;    Forest fires;    Hot spot;    Hotspots;    Indonesia;    Outlier Detection;    Sum of square errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/31/1/012035/pdf
DOI  :  10.1088/1755-1315/31/1/012035

来源: IOP

【 摘 要 】

Indonesia has serious problems in forest fires. One of the potential factors which indicates forest fires is hotspot. Hotspot is a forest fires indicator that detects a location with relatively higher temperature in comparison with nearby positions. One possible prevention efforts for forest fires is by detecting outliers on hotspots data. This study detects outlier on hotspots data in Riau Province in between year 2001 to 2012 using the DBSCAN algorithm and determines the distribution of outlier hotspots by region and time. The experiment results show that the highest occurrence of outliers is in 2005. The number of outliers on hotspots data reaches 1241 hotspots with the sum of square error (SSE) is 0.084. Outlier hotspots in Riau Province in 2005 spread across 11 districts/cities and 136 districts. In 2005 the highest outlier are found in Rokan Hulu with the number of outliers is 186 points. The highest frequency of hotspot that is considered as outliers is found in August 2005, with a total of 355 outliers in which as many 97 of these outliers are occurred in Rokan Hulu District.