A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s11783-023-1622-3.pdf
Reference49 articles.
1. Adamowski J F (2008). Peak daily water demand forecast modeling using artificial neural networks. Journal of Water Resources Planning and Management, 134(2): 119–128
2. Amari S I, Wu S (1999). Improving support vector machine classifiers by modifying kernel functions. Neural networks: the official journal of the International Neural Network Society or Neural Netw, 12(6): 783–789
3. Bedi J, Toshniwal D (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238: 1312–1326
4. Billings R B, Jones C V (2011). Forecasting Urban Water Demand. Washington, DC: America Water Works Association
5. Boggess A, Narcowich F J (2015). A first course in wavelets with Fourier analysis. 2nd ed. John Wiley & Sons, 183–217
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