Ensemble Learning Paradigms for Flow Rate Prediction Boosting

In response to the issue of water scarcity in recent years, international organizations, in collaboration with many governments, have initiated several drinking water supply projects carried out by geophysical and drilling companies. Unfortunately, despite the reliability of electrical resistivity profiling (ERP) and vertical electrical sounding (VES) methods, the substantial financial losses incurred due to numerous unsuccessful drillings are owing to the difficulty to emphasize the drilling location properly. Therefore, we proposed the ensemble machine learning (EML) paradigms to predict the flow rate (FR) with an optimal score before any drilling operations. The approach was experimented in a region with severe water shortages. Thus, geo-electrical features from the ERP and VES were defined and coupled with borehole data to create the binary dataset (FR <= 1m(3)/hr and FR > 1m(3)/hr for unproductive and productive boreholes respectively). Then, the dataset is state-of-art transformed before feeding to the EML algorithms. The model performance and generalization capability were evaluated using the Matthews correlation, the accuracy, the confusion matrix, the binary predictor error, the precision-recall, and the cumulative gain plot. As a result, the benchmark, pasting, extreme gradient boosting, and stacking paradigms have built a powerful range of FR prediction scores between 90 similar to 96%. Henceforth, the robust EML paradigms can be used to identify the best location for drilling operations, lowering the repercussion of unsuccessful drillings.

Automated summary

In response to water scarcity, international organizations and governments collaborated on drinking water supply projects using geophysical and drilling companies. Financial losses from unsuccessful drillings due to difficulty in locating drilling sites were reduced by using ensemble machine learning (EML) paradigms to predict flow rate (FR) scores before drilling operations. The approach was tested in a water-scarce region and achieved FR prediction scores of 90-96%. EML paradigms can aid in identifying optimal drilling locations and reducing the impact of unsuccessful drillings.