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Deep multi-task learning for early warnings of dust events implemented for the Middle East

Earth Sciences

Deep multi-task learning for early warnings of dust events implemented for the Middle East

R. Sarafian, D. Nissenbaum, et al.

This groundbreaking study by Ron Sarafian, Dori Nissenbaum, Shira Raveh-Rubin, Vikyhat Agrawal, and Yinon Rudich introduces a deep multi-task learning model for forecasting dust events in Israel, achieving a remarkable 76% detection rate a full day in advance. Dive into the analysis of local and regional PM₁₀ dynamics and uncover the critical meteorological factors driving dust occurrences.

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~3 min • Beginner • English
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