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http://dx.doi.org/10.9717/kmms.2020.23.10.1286

Analysis Model Evaluation based on IoT Data and Machine Learning Algorithm for Prediction of Acer Mono Sap Liquid Water  

Lee, Han Sung (School of Computer Eng., Youngsan University)
Jung, Se Hoon (School of Creative Convergence, Andong National University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.10, 2020 , pp. 1286-1295 More about this Journal
Abstract
It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.
Keywords
Acer Mono Sap; Analysis Model; IoT Data; Machine Learning; Hadoop;
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Times Cited By KSCI : 3  (Citation Analysis)
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