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Introducing a new clustering-based method for regionalization framework for continental-scale rainfall estimates from soil moisture dynamics using machine learning methods
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DOI：10.1016/j.agrformet.2025.110766.png)
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Abstract
En 中文
• Proposes a clustering-based framework for regionalizing SM2RAINNWF parameters. • Removes site-specific calibration needs using supervised and unsupervised methods. • Exploiting K-means clustering, rainfall intensity classification, and genetic algorithm to optimize rainfall estimation. • Enhancing scalability and adaptability of rainfall estimation in data-scarce regions through clustering-based approaches. • Improves SM2RAINNWF accuracy by 20 % NS and reduces RMSE by 10 % over CONUS.
Keywords:
clustering
SM2RAINNWF
rainfall estimation
data-scarce regions
genetic algorithm
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