A swelling pressure prediction model based on boruta feature selection mila region

Abstract

Swelling soils are problematic soil types that are prevalent across the globe. It ‎was noted that the costs ‎associated with damages caused by distended soils are ‎relatively high and this issue cannot be ignored. ‎Swelling pressure is a ‎fundamental parameter in the prediction of the swelling capacity of expansive ‎soils. In ‎machine learning, feature selection methods allow us to reduce computation time, enhance prediction accuracy, ‎and gain a deeper comprehension of the ‎data. In this paper, the Boruta algorithm is used to remove iteratively ‎the features ‎which are proved by a statistical test to be less relevant from 15 geotechnical ‎variables to predict ‎swelling pressure. The remaining variables are ‎inputs of a neural networks model (ANN). Results based on R ‎squared ‎determination coefficient, RMSE, MAPE, MSE, and RRSE show an ‎improvement of the neural model ‎by considering selected features by the Boruta ‎algorithm compared to the one without feature selection.‎ This approach highlights the effectiveness of feature selection in enhancing machine learning models for geotechnical applications.