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一个基于特征选择的混合模型,用于准确地空间预测沉积物中的潜在有毒元素:应用于中国Poyang湖

Guanghui Guo¹, Meiduo Anjiang², Ruiqing Zhang³

¹Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of hazardous materials

|October 2, 2025

概括

预测沉积物中的潜在有毒元素 (PTEs) 对水生生物的健康至关重要. 这项研究使用混合机器学习和地理统计方法提高了PTE预测的准确性,提高了环境保护战略.

科学领域:

环境科学环境科学
地质化学地质化学
生态生态学生态生态学

背景情况:

沉积物中的潜在有毒元素 (PTEs) 对水生生态系统构成风险.
精确地预测PTE的空间是必不可少的,但由于复杂的环境因素,这是一个挑战.

研究的目的:

开发一个综合框架,结合机器学习 (ML) 和普通 kriging (OK) 改进PTE预测Poyang湖沉积物.
为了确定最佳的ML模型和PTE空间分布的关键预测因素.

主要方法:

评估了三个ML模型:随机森林 (RF),极端梯度增强 (XGBoost) 和支持矢量机 (SVM).
应用特征选择技术 (相对重要,递归特征消除) 来识别重要的预测因素.
开发了一种混合RF-OK模型,结合了选定的预测因素.

主要成果:

射频模型在PTE预测方面表现出卓越的性能 (R2>0.70).
对于每个PTE,确定了不同的预测器集,增强了模型的特异性.
与OK和反向距离权重相比,RFOK模型显著提高了预测准确性,捕捉了微细尺度的变化.

结论:

在复杂的水生环境中,RFOK杂交有效地提高了PTE预测的准确性.
特性选择对于完善空间预测模型至关重要.
这种方法为有针对性的PTE污染控制策略提供了科学基础.

关键词:

环境预测因素环境预测因素机器学习是机器学习.可能有毒的元素.沉积物沉积物是一种沉积物.空间分布预测的预测

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