Mining Sequence Pattern on Hotspot Data to Identify Fire Spot in Peatland

Abstract

Fires on peatland occurs each year in Sumatra Island, Indonesia. A hotspot is an indicator of fire occurrences in dry land or in peatlands. Peatland fires can be identified by extracting sequential patterns in a hotspot dataset. Hotspots occurred two to five days consecutively have high potency to become fire spots indicating real fires (fire spots). This work aims to generate sequential patterns on hotspot datasets in Sumatra Island Indonesia in 2014 and 2015 using the SPADE algorithm. In addition association rule mining was conducted to obtain association between the locations of hotspot sequences and weather conditions. The results show that sequence patterns of hotspot in Sumatra in 2014 were occurred in the villages which have the weather conditions: average humidity between 70.1% and 78.2%, average temperature between 26.50˚C50˚C and 27.89˚C, and precipitation of 0 and 0.9 mm. In addition, sequence patterns of hotspot in Sumatra in 2015 were occurred in the villages which have the weather conditions: average humidity between 68.6 and 77.7%, average temperature between 27.00 and 27.69˚C, and precipitation of 0 mm. The sequence patterns of hotspot are strong indicator for fire spots. Identifying the sequence patterns of hotspots associated with the weather conditions of the locations where the sequences occurred will be beneficial for related parties in peatland fires prevention.