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Machine learning aided forecasting of subseasonal drought indicators in the European alps

https://www.wsl.ch/de/projekte/maschinelles-lernen-unterstuetzt-die-vorhersage-von-sub-saisonalen-trockenheitsindikatoren-in-den-europaeischen-alpen/

This research aims to explore the predictability of drought at a monthly time horizon in the form of low flow in Switzerland and the European Alps by using machine learning techniques and linking large scale weather patterns with local hydrological events.
Sub-Project 2: The second sub-project will extend the scope to the European Alps with the same

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Machine-Learning-Aided Forecasting of Drought-Related Extremes (MaLeFiX)

https://www.wsl.ch/de/projekte/malefix-machine-learning-aided-forecasting-of-drought-related-extremes/

Climate change exacerbates drought-related extremes, with significant ecological, economic, and human health impacts. MaLeFiX project is developing an interdisciplinary platform that will provide comprehensive four-week forecasts of drought-related extremes – buying stakeholders valuable time to act.
balance, and bark beetle dynamics have been refined to work seamlessly with the same

    Kategorien:
  • Wissen
  • International
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