Enhancing Water Demand Forecasting: Leveraging LSTM Networks for Accurate Predictions
Author:
Affiliation:
1. Building, Civil and Environmental Engineering Department, Concordia University, Montreal, QC H3G 2W1, Canada
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/69/1/120/pdf
Reference10 articles.
1. Billings, R.B., and Jones, C.V. (2011). Forecasting Urban Water Demand, America Water Works Association. [2nd ed.].
2. Pu, Z., Yan, J., Chen, L., Li, Z., Tian, W., Tao, T., and Xin, K. (2023). A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting. Front. Environ. Sci. Eng., 17.
3. Demand Forecasting in Water Supply Networks;Perry;J. Hydraul. Div.,1981
4. Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model;Ghiassi;J. Water Resour. Plan. Manag.,2008
5. 24-hours demand forecasting based on SARIMA and support vector machines;Braun;Procedia Eng.,2014
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