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Updated: Jan 30, 2026
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Predicción de riesgos ecológicos de metales pesados en cuencas hidrográficas basada en modelos interpretables de
Hong Chen1,2, Meiling Kong2, Zhenghua Wu2
1School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China.
Environmental monitoring and assessment
|January 28, 2026
Resumen
La generación de datos sintéticos utilizando SMOGN mejoró significativamente la precisión del modelo de aprendizaje automático para predecir la toxicidad de metales pesados en ecosistemas de cuencas hidrográficas. Este enfoque mejora la evaluación del riesgo ecológico y apoya las estrategias de control de la contaminación.
Palabras clave:
evaluación de riesgos ecológicosmetales pesadosmodelos de aprendizaje automáticoexplicaciones aditivas de ShapleyTécnica de sobremuestreo de minorías sintéticas para regresión con ruido gaussianoMás Videos Relacionados
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