A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

Abstract

Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new Memory-Based Learning (MBL) method performs better than the other methods, namely: Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR) and Boosted Regression Trees (BRT). For this purpose, we chose soil texture (contents of clay, silt and sand) as testing attributes. A selected set of soil samples, classified as Technosols, were collected from brown coal mining dumpsites in the Czech Republic (a total of 264 samples). Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross-validation was used to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R 2 cv ) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEP cv ). Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms. We found that the MBL performs better than the other three methods by about 10% (largest R 2 cv and smallest RMSEP cv ), followed by the SVMR. It should be pointed out that the other methods (PLSR and BRT) still provided reliable results. The study concluded that in this examined dataset, reflectance spectroscopy combined with the MBL algorithm is rapid and accurate, offers major efficiency and cost-saving possibilities in other datasets and can lead to better targeting of management interventions.

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

“…where w is a coefficient vector that determines the orientation of the hyperplane in the feature space, b is the offset of the hyper plane from the origin, and εi is the positive slack variables which represent the distance to misclassified support vectors from their respective marginal hyperplanes. For non-linear cases, SVM uses an implicit transformation of input variables via a kernel function [64,65],…”

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

“…Viscarra Rossel and Behrens [15] and Araujo et al [16] applied partial least square regression (PLSR), boosted regression trees (BRT) and support vector machine regression (SVMR) methods for the prediction of clay; SVMR offered the most successful prediction model due to its ability to solve the multivariate calibration problems and to reduce problems with heterogeneity and non-linearity. However, in a study by Gholizadeh et al [17], the memory based learning (MBL) technique outperformed PLSR, BRT and SVMR in soil texture prediction, which can be attributed to the selection of more appropriate neighbours to calibrate local models, as well as the inclusion of more suitable neighbours in each local model as a source of additional predictor variables [18]. Factors such as different populations, different partitions of the population for the analyses and environmental condition may also cause variation.…”

“…For example, absorption features in the VIS-NIR wavelength (400-1000 nm) are characteristics of the presence of soil carbon and iron oxide [34,35,[48][49][50], and those in the SWIR (1000-2500 nm) are from water, clay minerals and organic matter [16,51]. The important spectra absorption features through the use of some data mining algorithms have been studied by Viscarra Rossel and Behrens [15] and Gholizadeh et al [17]. Figure 5 demonstrates the spectra of soil samples based on their median, 1st and 3rd quartiles, as measured by both institutions protocols.…”

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

“…In a higher dimensional space, the SVM method needs an optimal linear hyperplane with the maximum margin for separating the given dataset. As the given data in a larger dimensional space can be complex, the kernel function is introduced to address this issue [49,50]:…”