CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data
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초록
In this paper, we propose an improved model to provide users with a better long-term prediction ofwaterworks operation data. The existing prediction models have been studied in various types of models suchas multiple linear regression model while considering time, days and seasonal characteristics. But the existingmodel 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 topredict data of water purification plant because its time series prediction is highly reliable. However, it isnecessary to reflect the correlation among various related factors, and a supplementary model is needed toimprove the long-term predictability. In this paper, convolutional neural network (CNN) model is introducedto 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. Inaddition, 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.
Abstract
In this paper, we propose an improved model to provide users with a better long-term prediction ofwaterworks operation data. The existing prediction models have been studied in various types of models suchas multiple linear regression model while considering time, days and seasonal characteristics. But the existingmodel 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 topredict data of water purification plant because its time series prediction is highly reliable. However, it isnecessary to reflect the correlation among various related factors, and a supplementary model is needed toimprove the long-term predictability. In this paper, convolutional neural network (CNN) model is introducedto 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. Inaddition, 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.
참고문헌 (8)
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