Pattern Extraction and Rule Generation of Forest Fire using Sliding Window Technique

Ku Ruhana Ku-Mahamud, Jia Yun Khor

Abstract


Patterns can be extracted from historical data and used to make predictions of future events. Such predictions are useful to support decision makers in various areas. In this study the sliding window technique is used to reveal forest fire patterns that relate four meteorological conditions (temperature, relative humidity, wind speed and rainfall) with burnt area size. Extracted patterns are then being grouped based on the size of burnt area. Rules are then generated resulting in eight distinct patterns of meteorological conditions that could predict the size of forest fire. Experimental results showed that extracted patterns produced good prediction accuracy.


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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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