Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy

• Published in: Geoderma Vol. 365; p. 114227
• Main Authors: Deiss, Leonardo, Margenot, Andrew J., Culman, Steve W., Demyan, M. Scott
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2020
• Subjects: chemometrics; clay; data collection; Error-grid; Fourier transform infrared spectroscopy; FTIR; Kernel; land use; least squares; Machine-learning; Midwestern United States; nonlinear models; prediction; RMSE; sand; soil properties; statistical models; support vector machines; Tanzania; total organic carbon; Midwestern United States; Tanzania; Error-grid; RMSE; Machine-learning; Kernel; FTIR

•Multivariate regression modeling is a prerequisite for mid-DRIFTS predictions.•Linear and non-linear models were compared along with non-linear parameterization.•Non-linear models outperformed linear models in predicting soil properties.•Tuning hyperparameters in non-linear models is a soil propert...