Review of deep learning on hydrological forecastingI：Common models and applying methods
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Abstract:
Data hydrology,led by the data-intensive research paradigm,is becoming an important research direction of hydrology.Deep learning,which performs well in extracting information from big data,has contributed significantly to the advancement of data-driven hydrological forecasting in recent years.It is continuously integrated with hydrology and becomes an indispensable tool in data hydrology.From a transdisciplinary view,the principle and structure of deep learning models commonly used in hydrology and the general workflow when applying them in hydrological forecasting are introduced.In addition,several approaches for integrating deep learning with domain knowledge in hydrology are described.A useful reference for researchers who are interested in exploring deep learning on hydrological forecasting provided.
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