The role of deep learning in urban water management: A critical review
文献类型：期刊论文
| 作者 | Fu, Guangtao1; Jin, Yiwen1; Sun, Siao2; Yuan, Zhiguo3; Butler, David1 |
| 刊名 | WATER RESEARCH
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 223页码:16 |
| 关键词 | Artificial intelligence Data analytics Deep learning Digital twin Water management |
| ISSN号 | 0043-1354 |
| DOI | 10.1016/j.watres.2022.118973 |
| 通讯作者 | Fu, Guangtao(g.fu@exeter.ac.uk) ; Sun, Siao(suns@igsnrr.ac.cn) |
| 英文摘要 | Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world. |
| WOS关键词 | NEURAL-NETWORKS ; DEFECT CLASSIFICATION ; ANOMALY DETECTION ; PREDICTION ; MODEL ; LOCALIZATION ; FRAMEWORK ; TIME |
| 资助项目 | Royal Society under the Industry Fellowship Scheme[IF160108] ; UK Engineering and Physical Sciences Research Council under the Alan Turing Institute[EP/N510129/1] ; National Natural Science Foundation of China[42071272] |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000879561000004 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | Royal Society under the Industry Fellowship Scheme ; UK Engineering and Physical Sciences Research Council under the Alan Turing Institute ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/186874]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Fu, Guangtao; Sun, Siao |
| 作者单位 | 1.Univ Exeter, Ctr Water Syst, Exeter EX4 4QF, Devon, England 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modelling, Beijing 100101, Peoples R China 3.Univ Queensland, Adv Water Management Ctr, St Lucia, Qld 4072, Australia |
| 推荐引用方式 GB/T 7714 | Fu, Guangtao,Jin, Yiwen,Sun, Siao,et al. The role of deep learning in urban water management: A critical review[J]. WATER RESEARCH,2022,223:16. |
| APA | Fu, Guangtao,Jin, Yiwen,Sun, Siao,Yuan, Zhiguo,&Butler, David.(2022).The role of deep learning in urban water management: A critical review.WATER RESEARCH,223,16. |
| MLA | Fu, Guangtao,et al."The role of deep learning in urban water management: A critical review".WATER RESEARCH 223(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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