Reliable multi-horizon water demand forecasting model
A temporal deep learning approach
Bibliographic Data
| ID | 21233418 |
|---|---|
| Authors | Ke Wang (0000-0002-4808-2637, Zhejiang Shuren University, corresponding author), Xiang Xie (0000-0002-0632-4626, Newcastle University), Banteng Liu (0000-0003-3341-3867, Zhejiang Shuren University), Jie Yu (0000-0001-5717-5520, State Key Laboratory of Industrial Control Technology), Zhangquan Wang (0000-0001-7437-0926, Zhejiang Shuren University) |
| Year | 2024 |
| Volume | 112 |
| Pages | 105595 |
| Publication date | 2024-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105595 |
| OpenAlex | W4399694066 |
| Language | EN |
| Citations received | 1 |
| References cited | 37 |
Deep learning · Demand forecasting · Econometrics · Economics · Horizon · Mathematical optimization · Operations research · Time horizon · Computer Science · Energy Load and Power Forecasting · Engineering · Environmental Science · Hydrological Forecasting Using AI · Mathematics · Water resources management and optimization · Artificial Intelligence
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Flow forecasting for leakage burst prediction in water distribution systems using long short-term memory neural networks and Kalman filtering
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |