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A cluster-based virtual sensing framework for estimating total nitrogen and total phosphorus using sensor-measurable water quality variables
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DOI：10.1016/j.jconhyd.2026.105024.png)
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Abstract
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• Cluster-based virtual sensing improve TN and TP estimation accuracy. • Spatial clustering captures heterogeneity in river water quality dynamics. • Cluster-specific models outperform conventional single-model approaches. • SHAP analysis reveals key predictors and cluster-dependent mechanisms. • Framework enables process-relevant interpretation of nutrient behavior.
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