CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data

Kerang Cao, Hangyung Kim, Chulhyun Hwang and Hoekyung Jung
Volume: 14, No: 6, Page: 1508 ~ 1520, Year: 2018
10.3745/JIPS.02.0104
Keywords: Big Data, CNN, Correlation Analysis, Deep-Learning, LSTM
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Abstract
In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

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Cite this article
IEEE Style
Kerang Cao, Hangyung Kim, Chulhyun Hwang, and Hoekyung Jung, "CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data," Journal of Information Processing Systems, vol. 14, no. 6, pp. 1508~1520, 2018. DOI: 10.3745/JIPS.02.0104.

ACM Style
Kerang Cao, Hangyung Kim, Chulhyun Hwang, and Hoekyung Jung, "CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data," Journal of Information Processing Systems, 14, 6, (2018), 1508~1520. DOI: 10.3745/JIPS.02.0104.