Journal of
Computational Biology and Bioinformatics Research

  • Abbreviation: J. Comput. Biol. Bioinform. Res
  • Language: English
  • ISSN: 2141-2227
  • DOI: 10.5897/JCBBR
  • Start Year: 2009
  • Published Articles: 39

Full Length Research Paper

Pattern clustering of forest fires based on meteorological variables and its classification using hybrid data mining methods

Yong Poh Yu1*, Rosli Omar1, Rhett D. Harrison2, Mohan Kumar Sammathuria3 and Abdul Rahim Nik4
1Department of Electrical Engineering, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia. 2Xishuangbanna Tropical Botanical Garden, Menglun, Mengla, 666303, Yunnan, China. 3Malaysia Meteorological Department, Jalan Sultan, 46667 Petaling Jaya, Malaysia. 4Forest Research Institute, 52110 Kepong, Selangor, Malaysia.
Email: [email protected]

  •  Accepted: 14 June 2011
  •  Published: 31 July 2011


This paper outlines two hybrid approaches to investigate the nonlinear relationship between size of a forest fire and meteorological variables (temperature, relative humidity, wind speed and rainfall). Self organizing map was used to cluster the historical meteorological variables. The clustered data were then used as inputs for two different approaches, the back-propagation neural network and the rule generation approaches. A back-propagation neural network was trained based on these inputs to classify the output (burnt area) in categorical form, namely; small, medium, large and extremely large. Several sets of rules were also generated from the data clustered by the self organizing map. Experimental results showed that both approaches gave considerable accuracy. Back-propagation neural network achieved a higher rate of accuracy than rule generation approach because the rule generation approach could not predict any criterion that goes beyond the set of rules.


Key words: Forest fire, self organizing map, back-propagation neural network, rule-based system.