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A comprehensive review of deep learning applications in hydrology and water resources
2020
Water Science and Technology
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
doi:10.2166/wst.2020.369
pmid:33341760
fatcat:ybb4agmq5vaejczzxzfsstz7wm