通过使用自动机器学习模型通过和多孔介质预测细菌运输
Fengxian Chen1, Bin Zhou2, Liqiong Yang1
1Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China.
Frontiers in microbiology
|May 26, 2023
概括
预测大肠杆菌在土壤中的传播对于防止地下水污染至关重要. 机器学习模型有效地利用土壤特性和水流变量预测细菌的移动,改善风险评估.
科学领域:
- 环境微生物学环境微生物学
- 水文地质学 水文地质学
- 数据科学是数据科学.
背景情况:
- 肥料修改土壤中的大肠杆菌 (大肠杆菌) 可以污染地下水.
- 预测细菌传输是减轻微生物风险的关键.
研究的目的:
- 开发机器学习模型,用于预测和多孔介质中的大肠杆菌传播.
- 为了确定影响细菌地下运动的关键变量.
主要方法:
- 编制了来自61项关于大肠杆菌传播的研究的377个数据集.
- 使用八个输入变量训练了六个机器学习算法.
- 基于细菌保留场景的评估模型性能.
主要成果:
- 机器学习模型有效地预测了大肠杆菌的运输,尽管个体变量相关性很低.
- 梯度提升机和极端梯度提升显示出卓越的性能.
- 孔水速度,离子强度,中位粒大小和柱长是关键预测因素.
结论:
- 机器学习为评估大肠杆菌地下运输风险提供了有价值的工具.
- 数据驱动的方法可用于预测各种环境污染物的运输.
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