Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling

Abstract

Water drilling machines are used to drill boreholes in the ground to extract groundwater. The resources required for water drilling vary from region to region due to underground water table depth and ground soil layer. Water drilling on a hard underground soil layer requires different resources than a soft underground. The proposed study facilitates the drilling industry by selecting the region with a soft land layer and increasing the penetration rate. Furthermore, the number of days and water table depth prediction allows the drilling industry to estimate the depth of the water table and time resources to reach the water table at different locations. The classification techniques classify the region based on the soil land layer. Regression techniques are used for predicting water table depth and number of days. The experiments are performed on a borehole log dataset provided by a research organization. This study used Support Vector Machine, TabNet, and Deep Tabular models to predict the land soil layer and compare the results with our proposed Ensemble Weighted Voting Soil Layer Classifier (EWV-SLC). The performance of the classification model is evaluated using accuracy, Precision, Recall, and F1 Score. The experimental finding shows that the EWV-SLC model performs better in accuracy and F1 score than other machine learning techniques. The performance of the regression model is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE),Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). In a days and water table depth prediction phase Support, Vector Regressor, Deep Neural Network, and TabNet Regressor are used, and compare the results with our proposed Ensemble Number of Days (E-NOD) and Ensemble Water Table depth (E-WTD) Regressor model. E-NOD and E-WTD models achieved less MAE, RMSE, and MSE than other machine learning methods.

Keywords

• Applied machine learning
• ensemble learning
• voting classifier
• deep tabular model