1. Combination of machine learning and VIRS for predicting soil organic matter
- Author
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Ni Wang, Jinbao Liu, Jiancang Xie, Zhenyu Dong, and Jichang Han
- Subjects
Coefficient of determination ,Soil test ,Mean squared error ,Artificial neural network ,business.industry ,Stratigraphy ,Soil organic matter ,04 agricultural and veterinary sciences ,010501 environmental sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,Weighting ,Support vector machine ,Partial least squares regression ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Artificial intelligence ,business ,computer ,0105 earth and related environmental sciences ,Earth-Surface Processes ,Mathematics - Abstract
Visible-near-infrared spectroscopy (VIRS) is one of the most promising alternative techniques for soil organic matter (SOM) due to its direct response. In this study, partial least squares regression (PLSR), support vector machine (SVM), artificial neural networks (ANNs), and Cubist combined with VIRS were utilized to develop the calibration model and evaluate the ability of machine learning models to predict soil organic matter content. A total of 190 surface soil samples (earth-cumulic-orthic anthrosols) were collected from the Weihe Plain of Shaanxi Province, China. The Kennard–Stone (KS) algorithm was employed to divide them into calibration and validation data. Moreover, the successive projections algorithm (SPA), competitive adaptive weight weighting algorithm (CARS), and their combination (SPA + CARS) were utilized to select characteristic wavelengths and improve the predictive ability of the model. Different evaluation indices, including root mean square error (RMSE), coefficient of determination (R2), the ratio of the performance to deviation (RPD), and the ratio of performance to interquartile range (RPIQ), were adopted to evaluate the accuracy of the model. In all cases, the AFS-SPA + CARS-Cubist method outperformed the PLSR, SVM, and ANN. For the Cubist model, the Rv2, RPD, and RPIQ ranged from 0.8629 to 0.9782, 0.8720 to 3.0203, and 2.005 to 4.4164, respectively. According to the results, combining VIRS with Cubist could accurately determine the SOM of earth-cumulic-orthic anthrosol soils of the Weihe Plain, China. Furthermore, SPA + CARS provided more precise calibration–validation models than SPA and CARS.
- Published
- 2021