Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan. (September 2022) - Rasool, Umair
Abstract: Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain. Graphical abstract: Image 1 Highlights: The provincial capital Quetta, Baluchistan, Pakistan was selected for present study. For GW productivity potential mapping, geology and slope degree are very important. ANN, RF and XGB models are best fitted models in the prediction. The ROC and AUC provided the prediction performance accuracy of the selected models. Correlation coefficient, RMSE and MAE provided the useful information for model performance accuracy.