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在土壤-大米系统中对重金属预测进行基于学习的应用研究

Huijuan Hao¹, Panpan Li², Wentao Jiao¹

¹Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.

The Science of the total environment

|July 14, 2023

概括

集体学习模型准确预测土壤和大米中的重金属度 (HMC),优于传统方法. 这些强大的模型为可持续的农田土壤管理和污染预防提供了解决方案.

科学领域:

环境科学环境科学
生态生态学生态生态学
农业科学农业科学

背景情况:

土壤中的重金属积累对生态系统和食品安全构成风险.
准确预测重金属度 (HMC) 是必要的,但具有挑战性.
传统模型往往缺乏有效管理所需的准确性和稳定性.

研究的目的:

开发和评估HMC在土壤-大米系统中的先进预测模型.
评估集合学习 (EL) 技术的性能,特别是随机森林 (RF) 和梯度增强机 (GBM),与基准模型对比.
调查基于EL的HMC预测模型的稳定性和实际适用性.

主要方法:

构建了一个包含490个多维环境共变量的数据集.
开发了EL-HMC模型 (RF-HMC,GBM-HMC) 并将它们与多重线性和贝叶斯回归 (BMs) 进行了比较.
使用R2,平均绝对误差 (MAE),根平均平方误差 (RMSE),灵敏度分析和空间自相关性 (SAC) 来进行评估.

主要成果:

与BM相比,EL-HMC模型的准确性明显更高 (土壤Cd的R2增加48.0%,大米Cd,Pb,Cr,Hg的R2增加58.2%).
RF-HMC和GBM-HMC实现了土壤Cd的R2值为0.654-0.690,大米重金属的R2值为0.618-0.850.
灵敏度和SAC分析证实了EL-HMC模型的卓越稳定性和稳定性.

结论:

集体学习技术为HMC提供了实用和可行的预测模型,具有卓越的准确性和稳定性.
开发的EL-HMC模型为可持续管理和准确预防农田重金属污染提供了新的视角.
这项研究强调了EL技术在污染生态学和环境管理方面的重大应用潜力.

关键词:

环境因素环境因素梯度增强机器梯度增强机器随机的森林随机的森林敏感度敏感度敏感度空间模式的空间模式

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