Data driven and expert knowledge approach for climate zoning
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Abstract
Increasing climate variability demands robust methodologies to characterize rainfall regimes, a critical factor for food security and water management. In Cuba, the application of advanced data mining techniques for this purpose remains an underexplored area. This study proposes a novel framework for clustering precipitation time series by combining automated feature extraction (tsfresh) with expert-defined hydroclimatic indicators, followed by the application of the Hierarchical Clustering Algorithm (HCA). The methodology, validated with data from 17 stations in Ciego de Ávila (1960-2013), identified six homogeneous rainfall regimes with significantly different seasonal patterns and extreme event intensities. This contribution enables precise climate zoning, surpassing traditional statistical approaches. The projection of this work is direct for the agricultural context, as it facilitates the development of differentiated cultivation strategies, optimized planting calendars, and improved climate risk management, thereby increasing the resilience of the Cuban agricultural sector.
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