Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …

Demand for accurate soil information is increasing for various applications. This paper
investigates the history of soil survey in Iran, particularly more recent developments in the …

Slope gradient and land use change are known to influence soil quality and the assessment
of soil quality is important in determining sustainable land-use and soil-management …

This study was conducted to evaluate the efficacy of the ensemble machine learning model
to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous …

Salinization and alkalization are predominant environmental problem world-wide which their
accurate assessment is essential for determining appropriate ways to deal with land …

Traditional soil salinity studies, especially over large areas, are expensive and time-
consuming. Therefore, it is necessary to employ new methods to examine salinity of large …

Soil texture is the most important soil physical property that determines water holding
capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher …

This paper assesses hybrid spatial models with the use of auxiliary variables based on
machine learning algorithms for predicting soil Organic Matter (OM) content in Kastoria area …

Digital soil mapping involves the use of auxiliary data to assist in the mapping of soil
classes. In this research, we investigate the predictive power of 6 data mining classifiers …

Abstract The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological
significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB) …