The main objective of this study was the measurement of soil erosion at micro-scale and macro-scale using laboratory experiments, field work, and remote sensing methods within a critical region of fire-affected forests on the southwestern coast of the Caspian Sea in the Guilan province of northern Iran.
The aim of this research was to evaluate and test the suitability of spline functions and spatial data-mining models to predict vertical and horizontal distributions of soil PSFs. In addition, we explored whether improvements in prediction could be achieved with the use of two techniques for input selection (i.e. ant colony optimization and correlation-based feature selection).
The aim of this research was to evaluate and test the suitability of spline functions and spatial data-mining models to predict vertical and horizontal distributions of soil PSFs. In addition, we explored whether improvements in prediction could be achieved with the use of two techniques for input selection (i.e. ant colony optimization and correlation-based feature selection).
In this paper, we applied a probabilistic optimization approach, namely DREAM, on Geonics EM38 data to explore the robustness of this approach for soil subsurface conductivity mapping. .
In this paper, we applied a probabilistic optimization approach, namely DREAM, on Geonics EM38 data to explore the robustness of this approach for soil subsurface conductivity mapping. .
The objectives of this study were to apply a k-NN approach to predict CEC in Iranian soils and compare this approach with the popular artificial neural network model. In this study, a data set of 3420 soil samples from different parts of Iran was used.
The objectives of this study were to apply a k-NN approach to predict CEC in Iranian soils and compare this approach with the popular artificial neural network model. In this study, a data set of 3420 soil samples from different parts of Iran was used.
This study aimed to map SOC lateral, and vertical variations down to 1 m depth. Six data mining techniques namely; artificial neural networks, support vector regression, k-nearest neighbor, random forests, regression tree models, and genetic programming were combined with equal-area smoothing splines to develop, and compare their effectiveness in achieving this aim. .
This study aimed to map SOC lateral, and vertical variations down to 1 m depth. Six data mining techniques namely; artificial neural networks, support vector regression, k-nearest neighbor, random forests, regression tree models, and genetic programming were combined with equal-area smoothing splines to develop, and compare their effectiveness in achieving this aim. .
Nine pedons and 30 surface samples were taken, described, and analyzed to investigate the effect of desertification on soil quality indices, mineralogical, and micromorphological properties of three regions (desert, semi-desert, non-desert) in central Iran.