A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands
Abstract: Predicting hotspot
occurrence as an indicator of forest and land fires is essential in developing an
early warning system for fire prevention. This work applied a spatial decision
tree algorithm on spatial data of forest fires. The algorithm is the
improvement of the conventional decision tree algorithm in which the distance
and topological relationships are included to grow up spatial decision trees.
Spatial data consisted of a target layer and ten explanatory layers
representing physical, weather, socio-economic and peatland characteristics in the
study area Rokan Hilir District, Indonesia. Target objects were hotspots of 2008
and non-hotspot points. The result was a pruned spatial decision tree with 122
leaves and the accuracy of 71.66%. The spatial tree has produced higher
accuracy than the non-spatial trees that were created using the ID3 and C4.5
algorithm. The ID3 decision tree had accuracy of 49.02% while the accuracy of
C4.5 decision tree reached 65.24%.
Author: Imas Sukaesih
Sitanggang
Journal Code: jptkomputergg140067
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