使用机器学习模型预测成熟度并识别有机废物堆肥的关键因素
Ning Wang1, Wanli Yang1, Bingshu Wang2
1Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China.
Bioresource technology
|April 7, 2024
概括
在堆肥中预测发芽指数 (GI) 现在通过机器学习更快. 随机森林和人工神经网络模型使用时间和温度等堆肥参数准确预测GI.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 在堆肥中测量发芽指数 (GI) 目前是一个缓慢且资源密集的过程.
- 准确的GI测量对于评估堆肥成熟度和质量至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测堆肥中的发芽指数 (GI).
- 确定影响GI预测的关键堆肥参数.
主要方法:
- 使用了四种ML模型:随机森林 (RF),人工神经网络 (ANN),支持向量回归 (SVR) 和决策树 (DT).
- 模型经过训练和验证,使用堆肥参数来预测GI.
- 为了确定特征的重要性,使用了SHapley添加式扩展 (SHAP).
主要成果:
- 射频和ANN模型显示出高预测性能,R2值超过0.9.
- SVR (<0.6) 和DT (<0.8) 显示 GI 的预测准确性较低.
- 堆肥时间,温度和pH被确定为影响GI的重要因素,堆肥时间是最具影响力的.
结论:
- RF和ANN是有效的ML工具,用于在堆肥过程中准确预测GI.
- 这种方法可以实现更高效和更智能的堆肥管理.
- 该研究提供了一种可靠的方法来快速评估GI,优化堆肥质量控制.
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