通过水文途径向土壤输入的区域估计:合过程模型和机器学习
Yutong Song1, Yiheng Wang2, Meie Wang2
1State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
Environmental pollution (Barking, Essex : 1987)
|August 15, 2025
概括
一个新的模型使用机器学习准确地预测了来自灌和洪水的土壤输入. 这种方法改善了重金属的区域风险评估和污染控制策略.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 土壤科学 土壤科学
背景情况:
- 现有的模型在重金属流动的时空分辨率方面扎,特别是在洪水期间.
- 准确地将污染源与复杂的水文系统中的水槽联系起来仍然是一个挑战.
研究的目的:
- 开发一个集成模型来量化 (Cd) 输入流通过灌和洪水路径在区域范围内.
- 克服现有模型在时空分辨率和洪水事件动态方面的局限性.
主要方法:
- 集成SWAT和向-扩散方程用于高分辨率Cd传输模拟.
- 在洪水事件期间估计Cd输入的MIKE FLOOD.
- 基于XGBoost的机器学习 (ML) 校正使用实证土壤Cd数据.
主要成果:
- 预计每年Cd输入流量:0.91毫克/平方米 (灌) 和4.637.95毫克/平方米 (洪水).
- 沉积物对Cd运输做出了重大贡献 (16.82%的灌,32.17%的洪水).
- 平均纠正的Cd输入流量为2.71毫克m−2·a−1,其中28.08%的农田受到组合输入的影响. 获得了ML校正R2 = 0.89.89.
结论:
- 这种新的综合模型为评估区域土壤流量提供了一个强大而可扩展的框架.
- 这种方法为有效的土壤风险管理和污染控制提供了宝贵的见解.
- 该模型增强了对重金属运输和沉积中的水文动态的理解.
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