Summary
This systematic review synthesises the application of deep learning methods across hydrology and water resources management, covering tasks such as prediction, generation, enhancement and classification. The authors evaluate state-of-the-art approaches and discuss key implementation challenges, including ethical implications for water governance decision-making, before proposing recommendations for future deployment of these techniques in the water sector.
Regional applicability
The review is global in scope and addresses methodological questions of relevance to water resources management across all geographies, including the United Kingdom. Applicability depends on data availability and computational infrastructure; UK water companies and regulators may find the synthesis of prediction and monitoring applications directly relevant to operational challenges.
Key measures
Classification and characterisation of deep learning methodologies; application domains in water resources; identified challenges in implementation including ethical considerations
Outcomes reported
A systematic review of deep learning methods applied to water sector challenges including monitoring, management, governance and communication. The review synthesised approaches for generation, prediction, enhancement and classification tasks in hydrology and water resources.
Topic tags
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