Application of data driven machine learning approach for modelling of non-linear filtration through granular porous media

Abstract

Modeling the relationship between volumetric flux (m3/s) and gradient of hydraulic head (m) is extremely challenging in case of non-linear filtration through porous packing. Due to the uncertainties associated with the definition and quantification of characteristic length and velocity, experimental, theoretical and numerical modelling approaches are not widely applicable. The machine learning algorithms have proven to be extremely useful for predicting similar situations when the physical process is too complex to understand. The performance of Artificial Neural Network (ANN), Random Forest (RF), and Boosted Tree methods have been investigated in the study for predicting the gradient of hydraulic head (target variable) in case of non-linear filtration through porous packing. Velocity of flow, media size, porosity, kinetic viscosity, and shape factor obtained from a wide range of reported data in the literature was used as input features. All three models were observed to predict the output values with significant accuracies (R2>0.90) for wide range of data obtained from different sources. A RF method based sensitivity analysis was performed to study the relative importance of different hydrological parameters over the target variable. These parameters in terms of a decreasing order was ranked as velocity, diameter, viscosity, porosity, and shape factor. These type of models can aid the researchers, planners, and designers to predict the hydraulic gradient or volumetric flux in multiple scenarios associated with non-linear filtration through porous media such as oil and gas exploration wells, water filters, rockfill dams etc.