Machine learning approaches for the prediction of soil aggregate stability
Yassine Bouslıhım, Aicha Rochdi, N. El Amrani Paaza
Abstract
. The purpose of this study is to compare the capabilities of the machine learning technique (Random Forest) and Multiple Linear Regression (MLR) to predict the Mean Weight Diameter (MWD) as an index of soil aggregate stability using soil properties from two sources data sets and remote sensing data. The performance of the models was evaluated using a 10-fold cross-validation procedure. The results achieved were acceptable in predicting soil aggregate stability and similar for both models. Thus, the addition of remote sensing indices to soil properties does not improve models. Results also show that organic matter is the most relevant variable for predicting soil aggregate stability for both models. The developed models can be used to predict the soil aggregate stability in this region and avoid waste of time and money deployed for analyses. However, we recommend using the largest and most uniform possible data set to achieve more accurate results.