《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
下一篇 上一篇
摘要
● A novel deep learning framework for short-term water demand forecasting.