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Frontiers of Environmental Science & Engineering >> 2023, Volume 17, Issue 2 doi: 10.1007/s11783-023-1622-3
A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China;1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China
Received：2022-03-03 Available online：2022-03-03Abstract
● A novel deep learning framework for short-term water demand forecasting.
Keywords
Short-term water demand forecasting ; Long-short term memory neural network ; Convolutional Neural Network ; Wavelet multi-resolution analysis ; Data-driven models
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