机器学习与地质探测器相结合,可以预测矿区土壤重金属的空间分布
Haolong Hu1, Wei Zhou1, Xiaoyang Liu2
1School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China.
The Science of the total environment
|December 29, 2024
概括
了解土壤重金属 (HM) 的分布是控制污染的关键. 将地质探测器模型与机器学习相结合,通过考虑空间数据异质性,改善了预测,提高了土壤污染评估.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 土壤科学 土壤科学
背景情况:
- 土壤重金属 (HM) 的精确空间分布对于有效的污染预防和整治至关重要.
- 传统的机器学习模型在环境数据中与空间分层异质性作斗争,影响了预测准确性.
研究的目的:
- 将地质探测器模型 (GDM) 与机器学习集成,以改善土壤重金属空间分布的预测.
- 解决传统模型的局限性,将空间异质性通过GDM衍生的共变量纳入.
主要方法:
- 结合地质探测器模型 (GDM) 与机器学习模型 (例如XGBoost).
- 使用GDM的因子检测用于共变量选,以解释局部空间异质性.
- 使用GDM的相互作用检测来构建空间分层的共变量,以解决特征异质性问题.
主要成果:
- 同变量选有效地将冗余特征降到最低.
- 空间分层的共变量显著改善了模型的预测性能 (R平方和RMSE).
- 在预测重金属分布方面,XGBoost模型表现出卓越的性能. 对于Pb的关键驱动因素包括pH和NDVI相互作用;对于Cr,DEM,pH和距离废物堆的距离至关重要.
结论:
- 拟议的GDM增强机器学习方法为空间重金属分布分析提供了更高的准确性.
- 这种方法为土壤重金属污染的驱动因素提供了宝贵的见解.
- 该方法适用于未来的土壤污染评估和环境管理,使得更精确的污染控制和整治策略成为可能.
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