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Predicting emitter irrigation duration for optimized irrigation system operation: An interpretable multi-model ML approach with SHAP
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DOI：10.1016/j.agwat.2026.110652.png)
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Abstract
En 中文
• An interpretable ML framework predicts emitter irrigation duration across water sources and designs. • XGBoost shows the best stability and lowest bias across cross-scenario conditions. • SHAP identifies sediment concentration as the dominant driver of emitter clogging. • Channel depth near 0.5 mm acts as an empirical indicator of clogging resistance. • Interpretable ML links prediction with data-driven anti-clogging emitter design.
Keywords:
Drip irrigation
Machine learning
SHAP
Emitter design
Sediment concentration
Threshold effect
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