چکیده (انگلیسی):

Developing a predictive model for forest fires occurrence is an important activity in a fire prevention program.
The model describes characteristics of areas where fires occur based on past fires data. It is essential as an
early warning system for preventing forest fires, thus major damages because of fires can be avoided. This
study describes the application of data mining technique namely decision tree on forest fires data. We
improved the ID3 decision tree algorithm such that it can be utilized on spatial data in order to develop a
classification model for hotspots occurrence. The ID3 algorithm which is originally designed for a non-spatial
dataset has been improved to construct a spatial decision tree from a spatial dataset containing discrete features
(points, lines and polygons). As the ID3 algorithm that uses information gain in the attribute selection, the
proposed algorithm uses spatial information gain to choose the best splitting layer from a set of explanatory
layers. The new formula for spatial information gain is proposed using spatial measures for point, line and
polygon features. The proposed algorithm has been applied on the forest fire dataset for Rokan Hilir district in
Riau Province in Indonesia. The dataset contains physical data, socio-economic, weather data as well as
hotspots and non-hotspots occurrence as target objects. The result is a spatial decision tree with 276 leaves
with distance from target objects to the nearest river as the first test layer and the accuracy on the training set
of 87.69%. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in
constructing a spatial decision tree from a spatial dataset. The algorithm results a predictive model for hotspots
occurrence from the real dataset on forest fires with high accuracy on the training set.