在LUCA土壤数据库中估计土壤有机碳使用基于混合内核高斯过程回归的Vis-NIR光谱学
Baoyang Liu1, Baofeng Guo1, Renxiong Zhuo1
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Communications Information Transmission and Convergence Technology Laboratory, Hangzhou 310018, China.
概括
本研究介绍了一种混合内核函数高斯过程回归 (HKF-GPR) 模型,用于准确地预测土壤有机碳 (SOC). 与传统方法相比,HKF-GPR模型在大型土壤光谱库中显著改善了SOC估计.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 土壤有机碳 (SOC) 对土壤健康和环境质量至关重要.
- 传统的SOC分析是缓慢而昂贵的.
- 可见近红外光谱 (Vis-NIR) 提供了一个更快的替代方案,但随着大规模样本异质性的增加,准确性会下降.
研究的目的:
- 为了比较部分最小平方回归 (PLSR),支向量回归 (SVR),随机森林 (RF) 和高斯过程回归 (GPR) 对于 SOC 预测的性能.
- 提出和评估一个具有混合内核功能 (HKF-GPR) 的新型GPR模型,以提高SOC估计.
- 为了应对大型土壤光谱数据库中预测精度降低的挑战.
主要方法:
- 使用Vis-NIR光谱数据进行SOC预测的PLSR,SVR,RF和GPR模型的比较.
- 开发了一个根据土壤光谱数据特征量身定制的混合内核函数 (HKF).
- 在大型土壤光谱数据库上实施和验证HKF-GPR模型.
主要成果:
- 高斯过程回归 (GPR) 在从大型光谱数据集中估计SOC方面表现优于其他测试模型.
- 提出的HKF-GPR模型实现了最高的准确性,其中R2=0.7671,RMSE=5.2934g/kg,RPD=2.0721,RPIQ=2.5789.
- HKF-GPR模型表现出卓越的性能和更广泛的SOC估计适用性.
结论:
- 在大规模的土壤光谱分析中,HKF-GPR模型是准确的SOC预测的高效工具.
- 这种方法克服了传统方法的局限性,并改进了现有的光谱技术.
- 开发的模型为有效地评估土壤质量提供了一个强大的解决方案.
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