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基于稳定的机器学习的先进三维预测模型用于土壤污染:来自中国南部污染地点的案例研究

Meiying Wang¹, Wenhao Zhao¹, Xiaochen Wu²

¹State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.

Journal of hazardous materials

|May 15, 2025

概括

一个新的3D插值模型通过整合空间数据和机器学习,准确地绘制了土壤污染的地图. 这种先进的方法改善了全球污染土地的地点评估和修复工作.

科学领域:

环境科学环境科学
地理空间分析是什么
机器学习机器学习

背景情况:

全球超过500万个地点面临土壤污染,需要准确的3D表征来进行风险评估和修复.
现有的3D插值方法难以结合空间相关性和异质性,这对于理解地下污染模式至关重要.

研究的目的:

开发一个精细的土壤污染3D插值模型,以考虑地点特征,空间位置,相关性和异质性.
在土壤污染模型中量化预测不确定性.
通过一种新的选择策略来提高模型的通用性和可靠性.

主要方法:

开发了一种机器学习 (ML) 模型,整合了网站特征,空间位置,空间相关性和空间异质性.
实施了一个稳定性分析框架,随机数据集分区和共变量排序用于模型选择.
进行了1000个随机模拟,用于模型选.

主要成果:

ML模型表现出高预测性能,四种重金属 (HM) 的R2值达到0.73以上.
稳定性分析和模型选择策略提高了模型的通用性.
开发的方法为选和选择准确的3D插值模型提供了可靠的基础.

结论:

介绍了对土壤污染的精确和多功能3D空间插值方法.
该模型的可访问的共变量有助于在现场评估和修复中广泛应用.
这项研究有助于提高全球污染场所管理的准确性和效率.

关键词:

现场污染污染污染空间自相关性空间自相关性空间异质性空间异质性稳定的机器学习.三维插值的三维插值

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