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A hybrid model based on CNN and Bi-LSTM for urban water demand prediction

Water demand forecast is the basis of urban intelligent water supply, because the system is limited by nonlinear changes in the process of water consumption, the traditional prediction model has great impact in accuracy and stability. Even small changes in temperature and holidays periods can lead to abnormal changes in urban water use. To solve these problems, a hybrid model combining convolutional neural network and bidirectional long and short term memory network was adopted in this study. Corresponding corrective model is established for special situations such as weather natural changes and holidays. In order to extract the features of water quantity and climate data, these features are input into the Bi-LSTM network to predict the usage of urban water. This paper carries out a correlation analysis of historical water data and climatic factors that cause an impact on the usage of urban water. The previous five days water usage data and the daily maximum temperature were selected as the basis for the holiday correction model and the temperature correction model. Comparing the different models before and after correcting the deviation, the prediction results have been improved. The present work was compared with results of long-term and short-term memory networks (LSTM), bidirectional long-term memory networks (Bi-LSTM), CNN, sparse autoencoder (SAEs), and CNN-LSTM, hence the prediction error is reduced by using the CNN-Bi-LSTM model. Finally, under the same training period, the training time and convergence of the six models were analyzed. The training time of CNN-Bi-LSTM is less than LSTM, Bi-LSTM, CNN, and CNN-LSTM, but larger than SAEs. The training convergence of CNN-Bi-LSTM was set in 125 times, which is smaller than the training times of the other five models.

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