算法与土壤之间的对话:机器学习揭开了土壤中甲酸盐污染的奥秘
Boyou Pan1, Jialin Lei1, Bogui Pan2
1College of Life Science and Technology, Jinan University, Guangzhou 510632, China; Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510632, China.
Journal of hazardous materials
|November 23, 2024
概括
机器学习准确地预测土壤甲酸盐 (PAE) 积累,确定水文气象和土壤因素作为关键驱动因素. 这种方法为农业可持续性和粮食安全提供了有效的污染物风险评估.
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:
- 土壤被酸盐 (PAE) 污染对农业的可持续性和粮食安全构成风险.
- 评估土壤中PAE动态的传统方法耗时且效率低下.
- 开发先进的预测模型对于有效的环境管理至关重要.
研究的目的:
- 开发一个智能机器学习框架,用于预测土壤PAE度.
- 确定影响土壤中PAE空间分布和积累的关键因素.
- 为风险评估和管理提供对未来PAE污染趋势的见解.
主要方法:
- 整合了30个特征,包括污染物水平,农业投入,土壤特性和气候数据.
- 开发和评估了六种机器学习模型:RFR,GBRT,XGBoost,MLP,SVR和KNN.
- 利用特征重要性和非线性效应分析来了解PAE影响因素.
主要成果:
- 多层感知器 (MLP) 模型实现了最高的预测准确性 (R2=0.8637).
- 确定了水文气象因素,土壤水分和营养特征是PAE分布的关键驱动因素.
- 在影响PAE水平的环境共变量之间发现了显著的协同作用.
结论:
- 机器学习为预测土壤PAE污染提供了有效和高效的方法.
- 未来的趋势表明,沿海地区的PAE水平正在下降,内陆地区的PAE可能会积累.
- 该研究为大数据时代的污染物风险评估和管理提供了新的视角.
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