使用以过程为导向的模型输出来增强基于机器学习的空间和时间的土壤有机碳预测
Lei Zhang1, Gerard B M Heuvelink2, Vera L Mulder3
1School of Geography and Ocean Science, Nanjing University, Nanjing, China; Soil Geography and Landscape Group, Wageningen University, Wageningen, the Netherlands.
The Science of the total environment
|February 9, 2024
概括
结合过程导向和机器学习方法的新混合模型提高了土壤有机碳 (SOC) 地图的准确性. 这种综合方法提高了空间和时间的预测,这对于气候变化研究和土壤管理至关重要.
科学领域:
- 土壤科学 土壤科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 土壤有机碳 (SOC) 动态对于气候变化研究和政策至关重要.
- 机器学习 (ML) 在空间土壤绘制方面表现出色,但在时间动态方面却很困难.
- 过程导向 (PO) 模型以机械方式捕捉时间 SOC 变化.
研究的目的:
- 开发和测试一个融合PO和ML的混合模型,用于时空SOC库存预测.
- 提高SOC绘图的准确性和物理可信性.
- 支持土壤管理和气候变化下的政策决策.
主要方法:
- 开发了一种混合模型,将PO和ML结合起来,用于表土SOC库存预测.
- 使用PO模型预测作为ML模型在未采样年中的训练数据.
- 使用权重参数来平衡PO模型输出和实际测量.
主要成果:
- 从PO和混合模型的时间趋势相似,与单独的ML模型不同.
- 混合动力车型以0.29公斤米-2.2的RMSE实现了最佳性能.
- 与ML模型相比,预测准确度有19%的改善.
结论:
- 混合框架增强了时空土壤碳绘图的准确性和物理可信性.
- 这种综合方法为土壤管理策略提供了宝贵的见解.
- 该模型为应对气候变化和人类对土壤的影响提供了一个强大的工具.
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