Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra

Full text
Author(s):
Santana, Everton Jose [1] ; dos Santos, Felipe Rodrigues [2] ; Mastelini, Saulo Martiello [3] ; Melquiades, Fabio Luiz [2] ; Barbon Jr, Sylvio
Total Authors: 5
Affiliation:
[1] State Univ Londrina UEL, Dept Comp Sci, Londrina, Parana - Brazil
[2] State Univ Londrina UEL, Dept Phys, Londrina, Parana - Brazil
[3] Univ Sao Paulo, Inst Math Sci & Comp, Sao Carlos - Brazil
Total Affiliations: 3
Document type: Journal article
Source: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS; v. 209, FEB 15 2021.
Web of Science Citations: 0
Abstract

Energy dispersive X-ray fluorescence (EDXRF) is one of the most quick, environmentally friendly and least expensive spectroscopic analytical methodologies for assessing soil quality parameters. However, challenges in EDXRF spectral data analysis still demand more efficient methods. One possible solution is using Machine Learning (ML), particularly Multi-target Regression (MTR) methods, which predict multiple parameters taking advantage of inter-correlated parameters. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors 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 Regression (SVR) 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 SVR with a radial kernel, the prediction of base saturation percentage was improved by 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%. (AU)

FAPESP's process: 18/07319-6 - Multi-target data stream mining
Grantee:Saulo Martiello Mastelini
Support Opportunities: Scholarships in Brazil - Doctorate