CLINT – MedEWsa Webinar
, h. 15:00 CET
To join the webinar, register here
Hydrological models and services are essential for water management, flood and drought preparedness, and ecosystem protection. However, large-scale process-based models often struggle with local-scale accuracy due to the complexity of hydrological processes, uncertainties in meteorological inputs, and human interventions.
This webinar dives into how state-of-the-art artificial intelligence (AI) and machine learning (ML) can enhance streamflow predictions across Europe’s diverse hydro-climatic and geographic conditions. We will explore hybrid approaches that integrate traditional hydrological modeling with ML post-processing techniques, such as Long Short-Term Memory (LSTM) model and Random Forest, to improve predictive accuracy. Furthermore, we will present AI-driven regionalization strategies designed to enhance streamflow forecasting in data-scarce and ungauged regions.
Join us to explore how these innovations are shaping the future of hydro-climate services, enabling more reliable and accessible water management solutions, and strengthening early warning capabilities in a changing climate.
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