文章基本信息

标题：LINKED OPEN GOVERNMENT DATA AS BACKGROUND KNOWLEDGE IN PREDICTING FOREST FIRE
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作者：GURUH FAJAR SHIDIK ; AHMAD ASHARI
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：62
期号：3
出版社：Journal of Theoretical and Applied
摘要：Nowadays with linked open data, we can access numerous data over the world that more easily and semantically. This research focus on technique for accessing linked open government data LOGD from SPARQL Endpoint for resulting time series historical of Forest Fire data. Moreover, the data will automatically uses as background knowledge for predicting the number of forest fire and size of burn area with machine learning. By using this technique, LOGD could be used as an online background knowledge that provide time series data for predicting trend of fire disaster. In evaluation, mean square error MSE and root mean square error RMSE are used to evaluate the performance of prediction in this research. We also compare several algorithm such as Linear Regression, Neural Network and SVM in different window size.
关键词：Linked Open Government Data; Forest Fire Prediction; Time Series Data; Data Mining.