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Deep learning for water quality
Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …
context of intensifying climate extremes expected in the future. These challenges arise partly …
Challenges in modeling and predicting floods and droughts: A review
Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …
time scales to develop management strategies targeted at minimizing negative societal and …
[HTML][HTML] Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores
A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe
efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is …
efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is …
[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
What role does hydrological science play in the age of machine learning?
This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
[HTML][HTML] Deep learning rainfall–runoff predictions of extreme events
The most accurate rainfall–runoff predictions are currently based on deep learning. There is
a concern among hydrologists that the predictive accuracy of data-driven models based on …
a concern among hydrologists that the predictive accuracy of data-driven models based on …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
Caravan-A global community dataset for large-sample hydrology
High-quality datasets are essential to support hydrological science and modeling. Several
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy
Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …
water resources management as well as downstream applications such as ecosystem and …