Abstract
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.
Highlights
Neighbouring countries such as Malaysia and Singapore
This study presents an extended ID3 algorithm to create a classifier namely spatial decision tree from spatial data
A spatial dataset is organized in a set of layers in which the layers are grouped into two categories i.e., explanatory layers and a target layer
Summary
Neighbouring countries such as Malaysia and Singapore. Fire prevention has an important role in minimizing the Forest fires in various parts in Sumatera and Kalimantan, Indonesia occur every year especially in dry season. The GIS-based method of Complete Mapping Analysis (CMA) is applied in (Boonyanuphap et al, 2001) to create the wildfire risk model for the area of Sasamba in East Kalimantan in Indonesia. GISs and remote sensing are used to analyze forest fire data to create forest fires risk models for some regions in Indonesia (Hadi, 2006; Danan, 2008). Criteria evaluation and weighting methods, such as Complete Mapping Analysis (CMA) and Multi-criteria Analysis (MCA), are most applied to evaluate small problems containing few criteria This situation has lead to the increasing in applying data mining techniques to extract interesting spatial patterns from large spatial data. Data mining tasks including association rules mining, classification and prediction, as well as cluster analysis have been successfully employed to analyse spatial data related to forest fires (Tay et al, 2003; Stojanova et al, 2007; Yu and Bian, 2007; Kalli and Ramakrishna, 2008; Hu et al, 2009)
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