Estimation of the recharging rate of groundwater using random forest technique

Abstract

Accurate knowledge of the recharging rate is essential for several groundwater-related studies and projects mainly in the water scarcity regions. In this study, a comparison between different methods of soft computing-based models was obtained in order to evaluate and select the most suitable and accurate method for predicting the recharging rate of groundwater, as the natural recharging rate of the groundwater is important in efficient groundwater resource management and aquifer recharge. Experimental data have been used to investigate the improved performance of Gaussian process (GP), M5P and random forest (RF)-based regression method and evaluate the potential of these techniques in the prediction of natural recharging rate. The study also compares the prediction of recharging rate to empirical (Kostiakov model, multilinear regression, multi-nonlinear regression) equations. The RF method was selected for the recharging rate prediction and was compared with the M5P tree, GP and also empirical models. While GP, M5P tree and empirical models provide good quality of prediction performance, RF model showed superiority among them with coefficient of correlation (R) values as 0.98 and 0.91 for training and testing, respectively. Out of 106 observations collected from laboratory experiments, 73 were used for developing different models, whereas rest 33 observations were used for the assessment of the models’ performance. Sensitivity analysis recommends that time parameter (t) is the main influencing parameter, which is crucial for the prediction of the recharging rate. RF-based model is suitable for accurate prediction of recharging rate of groundwater.

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

“…It is based on the linear relationships between inputs and outputs. In other words, it extracts the linear relationships between dependent and independent variables by involving a regression that is constant in the formula (Sihag et al, 2020). MLR work is based on the equation below:…”

“…The basis of this algorithm is based on dividing the overall problem into smaller problems by dividing the data, so that a multivariate model is constructed for each small problem and assigning linear regression functions into the final nodes. This method is characterized by its ability to deal with complex problems with many variables, with the condition that they are continuous class problems instead of discrete classes (Adnan et al 2021;Sihag et al 2020;Singh et al 2017).…”

Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method

“…Investigation of the infiltration process in the soil is of great importance in the study of water engineering projects including irrigation and drainage networks, flood potential assessment, artificial recharge and wastewater disposal location (Sihag et al, 2020). The choice of irrigation system type for each area depends on the characteristics of infiltration into the soil.…”

Prediction of Undrained Shear Strength of Crushed Tire Mixture with Fine-Grained Soil by using Machine Learning Approaches

“…From last few decades, soft computing techniques such as artificial neural network (ANN), Gaussian process regression, Support vector machines (SVM)) and M5 model tree were successfully used in solution of various engineering related problems (Parsaie and Haghiabi 2016;Mohanty et al, 2019;Kumar and Sihag 2019;Al-Gabalawy et al, 2021 a and b;Salmasi et al, 2021;Sihag et al, 2020;Pandhiani et al, 2021;Bhoria et al, 2021, Thakur et al, 2021Sangeeta et al, 2021). Sihag et al, (2019) used ANFIS, SVM and random forest (RF) for the prediction of cumulative infiltration (CI) and infiltration rate (IR) in arid areas in Iran.…”