基于EDXRF光谱的多目标叠加综合改进土壤性质预测
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
摘要：
机器学习（ML）算法已被用于评估土壤质量参数以及非破坏性的方法。在光谱分析方法中，能量色散X射线荧光（EDXRF）与传统方法相比是更快速，环保和更便宜的方法之一。然而，EDXRF光谱数据分析中的一些挑战仍然需要能够提供准确结果的更有效的方法。使用多目标回归（MTR）方法，可以预测多个参数，并且还可以利用相互关联的参数来提高整体预测性能。在这项研究中，我们提出了多目标堆叠概括（MTSG），这是一种新的MTR方法，它依赖于从堆叠结构中排列的不同回归变量中学习，以获得提升的结果。比较了MTSG和5种MTR方法对10个土壤肥力参数的预测效果。随机森林和支持向量机（具有线性和径向核）被用作嵌入到每个MTR方法中的学习算法。结果表明，MTR方法优于单目标回归（传统的ML方法），减少了5个参数的预测误差。尤其是MTSG对磷、总有机碳和阳离子交换量的误差最小。径向核支持向量机的相对性能，碱饱和度的预测提高了19%。最后，所提出的方法能够将分析所有目标的平均误差从0.67（单目标）降低到0.64，代表了4.48%的全局改进。
英文摘要：
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
