Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA

Abstract

The main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e.g. power, logarithmic and spline) and data miner methods (artificial neural network: ANN and support vector regression: SVR) were applied for three-dimensional mapping of soil organic matter (SOM) in Big Sioux River watershed, South Dakota, USA. Unsurprisingly, the ABC feature selection algorithm indicated that remote sensing data (e.g. NDVI) are powerful predictors at soil surface, however, with the increasing soil depth, the terrain parameters (e.g. wetness index) became more relevant. Our findings from this study demonstrated that both the spatial models generally performed well. The mean R2 values calculated by 10-fold cross validation suggested that SVR and ANN models could explain approximately 50 and 57% of total SOM variability, respectively. However, predictive power of both models increased when ABC feature selection algorithm applied, particularly when it combined with the ANN model. Results showed that DSM approaches are very important and powerful tool to explain the 3D spatial distribution of SOM across the study watershed.

Keywords

• digital soil mapping
• auxiliary variables
• support vector regression
• artificial neural networks
• south dakota