| Autor: |
Sit M; Interdisciplinary Graduate Program in Informatics, University of Iowa, Iowa City, USA and IIHR - Hydroscience & Engineering, University of Iowa, 100 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, Iowa 52242-1585, USA E-mail: muhammed-sit@uiowa.edu., Demiray BZ; Department of Computer Science, University of Iowa, Iowa City, USA., Xiang Z; Department of Civil and Environmental Engineering, University of Iowa, Iowa City, USA., Ewing GJ; Department of Civil and Environmental Engineering, University of Iowa, Iowa City, USA., Sermet Y; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA., Demir I; Department of Civil and Environmental Engineering, University of Iowa, Iowa City, USA. |
| Abstrakt: |
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources. |
