Artificial intelligence in hydrology

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Editorial: artificial intelligence in hydrology

Nowadays, hydrological systems are becoming increasingly complex owing to the growing interaction between nature and humans at the local scale of river sections, lakes, reservoirs, catchments, etc., to the global scale. There is great demand for the development of models to evaluate, predict, and optimize the performance of complex hydrological systems whose behavior is characterized by a strong nonlinearity. However, traditional approaches can hardly handle this nonlinear behavior; moreover, the analysis of hydrological systems at the large scale, even global, requires dealing with large-volume and realtime data. In recent years, artificial intelligence (AI), especially deep learning, has shown great potential to process massive data and solve large-scale nonlinear problems. AI has been successfully applied to computer vision, machine translation, bioinformatics, drug design, and climate science. AI models have produced results comparable to and even better than expert human performance. It is expected that AI can significantly contribute to hydrology research as well as development. This special issue is dedicated to presenting to the readership of Hydrology Research some of the latest advances in the field of AI in hydrology. After unbiased and rigorous peer reviews, eight articles were selected to be published in this special issue. Both theoretical and experimental articles are included, covering new and emerging AI methods and models from various challenging problems in hydrology. We hope this special issue can attract more attention from academia and industry to this exciting area

SOURCE IWA OPEN ACCESS LIBRARY

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Taxonomy

• Hydrology
• Hydrology
• artificial intelligence