Using tree-based machine learning models to predict diverse compost maturity via one-hot encoding: Model deployment, experimental validation, and practical application

doi:10.1016/j.wasman.2025.114981

Using tree-based machine learning models to predict diverse compost maturity via one-hot encoding: Model deployment, experimental validation, and practical application

2025-07-07

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DOI：10.1016/j.wasman.2025.114981

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• 结合多种材料和尺度的数据在树算法中实现了R2 > 0.93。 • 最佳模型AdaBoost在堆肥成熟度（GI）预测中达到R2 = 0.9720。 • 通过基尼指数（Gini Index）和SHAP分析了堆肥成熟度的关键特征。 • Stacking融合模型通过真实世界数据的验证实现了R2 = 0.9733。 • 创建了一个易于使用的桌面和移动端堆肥成熟度预测应用。

Using tree-based machine learning models to predict diverse compost maturity via one-hot encoding: Model deployment, experimental validation, and practical application

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