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《环境科学与工程前沿(英文)》 >> 2023年 第17卷 第2期 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

收稿日期: 2022-03-03 发布日期: 2022-03-03

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摘要

● A novel deep learning framework for short-term water demand forecasting.

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