Earth Sciences

Unveiling teleconnection drivers for heatwave prediction in South Korea using explainable artificial intelligence

Y. Lee, D. Cho, et al.

This study conducted by Yeonsu Lee, Dongjin Cho, Jungho Im, Cheolhee Yoo, Joonele Lee, Yoo-Geun Ham, and Myong-In Lee uncovers vital teleconnection drivers for predicting heatwaves in South Korea. By employing machine learning and explainable AI, the research identifies significant correlations between snow depth variability in key mountain regions and summer climate triggers, showcasing the power of advanced ML techniques in this domain.

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Abstract

This study investigates teleconnection drivers for heatwave prediction in South Korea using machine learning and explainable AI. The research reveals that snow depth variability in the Gobi Desert and Tianshan Mountains are crucial and predictable drivers, exhibiting high correlation with summer climate teleconnection triggers and regional variabilities. A Light Gradient Boosting Machine (LGBM) model outperforms other methods in predicting heatwave frequency, highlighting the advantages of advanced ML techniques in capturing complex relationships.

Publisher

Nature Communications

Published On

Aug 03, 2024

Authors

Yeonsu Lee, Dongjin Cho, Jungho Im, Cheolhee Yoo, Joonele Lee, Yoo-Geun Ham, Myong-In Lee

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