Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling

Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K -nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated that the LR and RF algorithms have produced the most accurate models for deep and shallow wells respectively. Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.