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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 …
[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 …
Global prediction of extreme floods in ungauged watersheds
Floods are one of the most common natural disasters, with a disproportionate impact in
develo** countries that often lack dense streamflow gauge networks. Accurate and timely …
develo** countries that often lack dense streamflow gauge networks. Accurate and timely …
[HTML][HTML] Deep learning for cross-region streamflow and flood forecasting at a global scale
Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.
Traditional physically based models are hampered by sparse parameters and complex …
Traditional physically based models are hampered by sparse parameters and complex …
[HTML][HTML] Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence
Accelerated progress in climate modeling is urgently needed for proactive and effective
climate change adaptation. The central challenge lies in accurately representing processes …
climate change adaptation. The central challenge lies in accurately representing processes …
HESS Opinions: Never train an LSTM on a single basin
Machine learning (ML) has played an increasing role in the hydrological sciences. In
particular, certain types of time series modeling strategies are popular for rainfall–runoff …
particular, certain types of time series modeling strategies are popular for rainfall–runoff …
A benchmark dataset for machine learning in ecotoxicology
The use of machine learning for predicting ecotoxicological outcomes is promising, but
underutilized. The curation of data with informative features requires both expertise in …
underutilized. The curation of data with informative features requires both expertise in …