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Predicting reference evapotranspiration using the weighted instance handler wrapper algorithm
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DOI：10.1016/j.ecoinf.2026.103684.png)
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Abstract
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• Developed a machine learning framework for reference evapotranspiration prediction. • Models trained at one station and validated across four additional stations. • Two different techniques used to identify optimal input combinations. • Efficient inputs reduced prediction uncertainty by up to 31 %. • Models showed reliable performance and transferability to similar regions.
Keywords:
Reference evapotranspiration (ETo)
Ensemble-based machine learning
WIHW-AMTree-PSO
Feature selection
Efficient input scenario
California
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7.3
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3.8K
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