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一个机器学习和基于实验的模型,用于预测土壤对氨酸的吸收能力

Xinyi Lai¹, Pengfei Zhou¹, Yi Kong¹

¹State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.

Environmental research

|December 13, 2023

概括

预测土壤中的疏水性有机污染物 (HOC) 吸附对于环境命运评估至关重要. 机器学习,特别是极端梯度增强 (XGBT),准确地估计了氨酸的吸收,有助于土壤管理.

科学领域:

环境化学环境化学
土壤科学土壤科学
计算化学计算化学

背景情况:

土壤吸附控制了疏水性有机污染物 (HOCs) 的环境命运.
预测HOC吸附需要一般化的模型,因为土壤属性变化.
南作为研究HOC吸附的模型化合物.

研究的目的:

开发和验证机器学习 (ML) 模型,用于估计土壤中的色素吸收.
确定最有效的ML方法来预测HOC吸附能力.
绘制中国土壤吸附能力的全国范围分布图.

主要方法:

从文献中提取了488个数据点来训练和测试ML模型.
评估了六种不同的ML算法,包括极端梯度增强 (XGBT).
通过20个中国土壤样本的实验分批吸附数据验证了最佳模型.

主要成果:

在测试数据集上,XGBT模型实现了高精度 (R2=0.91,RMSE=0.24).
在XGBT预测和实验取数据 (p=0.14) 之间没有发现统计学上显著的差异.
通过使用XGBT模型和土壤属性数据,成功地绘制了中国各地土壤吸附能力的分布图.

结论:

XGBT模型提供了一种强大而准确的方法来预测土壤中的HOC吸附.
这种方法提高了我们对陆地生态系统中持续有机污染物迁移的理解.
开发的模型支持更精确和科学的土壤环境管理策略.

关键词:

批量吸附试验批量吸附试验疏水性有机污染物疏水性有机污染物机器学习是机器学习.全国发行全国发行.土壤土壤土壤.

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