使用Vis-NIR光谱学与石灰质土壤中的机器学习算法相结合,预测矿井影响的土壤有机碳和总
Tianqi Zhang1, Ye Li1, Mingyou Wang2
1Key Laboratory of Eco-restoration of Regional Contaminated Environment of Ministry of Education, Shenyang University, Shenyang, 110044, Liaoning, China.
Scientific reports
|November 14, 2024
概括
可见近红外 (Vis-NIR) 光谱学准确预测土壤有机碳和总. 支持矢量机 (SVM) 模型在评估采矿对土壤营养物的影响方面表现优于部分最小平方回归 (PLSR).
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 环境监测 环境监测
背景情况:
- 采矿活动可以显著改变土壤特性,影响营养储备.
- 准确评估土壤有机碳 (OC) 和总 (TN) 对于了解土壤健康和环境影响至关重要.
- 可见近红外 (Vis-NIR) 谱学为土壤分析提供了一种快速而无损的方法.
研究的目的:
- 评估铁矿开采对土壤有机碳 (OC) 和总 (TN) 库存的影响.
- 使用Vis-NIR光谱法比较部分最小平方回归 (PLSR) 和支向量机 (SVM) 模型的预测性能.
- 确定对土壤OC和TN变化最敏感的光谱区域.
主要方法:
- 从铁矿附近的农业区收集了105个土壤样本 (受污染,中度污染,非污染的地点).
- 利用可见近红外 (Vis-NIR) 光谱仪获取光谱数据.
- 应用部分最小平方回归 (PLSR) 和支向量机 (SVM) 模型用于对土壤OC和TN进行定量分析.
主要成果:
- 没有污染的地点表现出最高的土壤OC库存 (7.5公斤m−2) 和TN (2.5公斤m−2),其次是中度污染的地点.
- 来自污染地点的土壤显示出最高的光谱反射率,500-700纳米之间的频段与OC有很强的相关性.
- 与PLSR相比,SVM模型为表层土壤和地下土壤OC和TN提供了更好的预测,对表层土壤OC和TN的准确性更好.
结论:
- 与SVM相结合的Vis-NIR光谱是预测土壤OC和TN的高效方法,性能优于PLSR.
- 采矿污染对土壤OC和TN库存产生负面影响,非污染地区的营养水平最高.
- SVM模型在评估土壤营养状况和环境影响方面表现强,特别是在表层土壤中.
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