A Comprehensive Review of Deep Learning Applications in Hydrology and
Water Resources
- URL: http://arxiv.org/abs/2007.12269v1
- Date: Wed, 17 Jun 2020 16:57:17 GMT
- Title: A Comprehensive Review of Deep Learning Applications in Hydrology and
Water Resources
- Authors: Muhammed Sit, Bekir Z. Demiray, Zhongrun Xiang, Gregory J. Ewing,
Yusuf Sermet and Ibrahim Demir
- Abstract summary: The global volume of digital data is expected to reach 175 zettabytes by 2025.
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 which 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.
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