Machine learning-based 1-year-lead summer rainfall prediction over the DPRK using regionally optimized spatial-contrast indices

YONG SIK HAM; Hyon Su Ri; Kyong-Bok Sonu; Sang‐Il Jong; Un Sim Paek

doi:10.1039/d6ea00029k

January 1, 2026Environmental Science AtmospheresOpen Access

Machine learning-based 1-year-lead summer rainfall prediction over the DPRK using regionally optimized spatial-contrast indices

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Key Points

The aim is to predict summer rainfall one year ahead in the northern part of the Korean Peninsula using machine learning techniques.
Utilized machine learning algorithms to analyze climatic data.
Employed regionally optimized spatial-contrast indices for prediction accuracy.
Focused on summer monsoon precipitation patterns in the region.
Achieved reliable predictions for summer rainfall one year in advance.
Improved disaster risk management strategies through enhanced forecasting accuracy.

Implication

Machine learning predicts summer rainfall in the northern Korean Peninsula, suggesting improved resource management.

Abstract

The northern part of the Korean Peninsula (NPKP) is a region highly dependent on summer monsoon precipitation for water resources and disaster risk management, and reliable one-year-ahead prediction of summer...

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Cite This Study

HAM et al. (2026) studied this question.

synapsesocial.com/papers/69db37ca4fe01fead37c5d65 https://doi.org/10.1039/d6ea00029k

View Full Paper

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Machine learning-based 1-year-lead summer rainfall prediction over the DPRK using regionally optimized spatial-contrast indices

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine learning-based 1-year-lead summer rainfall prediction over the DPRK using regionally optimized spatial-contrast indices

Key Points

Implication

Abstract

Discussion

Cite This Study

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