The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Artificial Neural Network-based Real Time Water Temperature Prediction in the Soyang River

인공신경망 기반 실시간 소양강 수온 예측

  • Jeong, Karpjoo (Dept. of Internet Engineering, Konkuk Univerity) ;
  • Lee, Jonghyun (Dept. of Internet Engineering, Konkuk Univerity) ;
  • Lee, Keun Young (Institute for Ubiquitous Information Technology and Applications, Konkuk University) ;
  • Kim, Bomchul (Dept. of Environmental Engineering, Kangwon University)
  • Received : 2016.10.31
  • Accepted : 2016.11.07
  • Published : 2016.12.01
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Abstract

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

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References

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