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.

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Data Availability

In this paper, the data are collected according to two separated ways including (i) telemetry infrastructure of water company in Mashhad City and (ii) real field measurement and data gathering. Finally, all required data is available for declaration.


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In this paper are financial issues and requirements are supplied by the International University of Imam Reza.

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In this paper, Mr. Mohammad Hossein Amini has programmed all soft systems, Mrs. Malihe Arab has designed and evaluated hydraulic computations, Mrs. Mahdieh Ghiyasi Faramarz has done field practices, Mr. Mohammad Gheibi has appraised empirical outcomes of research and operational concepts and finally, Dr. Adel Ghazikhani has oriented all aspects of investigation and he is the corresponding author. All authors consent to participate as per the above structure.

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Correspondence to Adel Ghazikhani.

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In this paper, all authors agree with publishing this paper in the title “Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling” in the special issue of “Supply Chain Network Design (SCND)” in Environmental Science and Pollution Research journal.

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Amini, M.H., Arab, M., Faramarz, M.G. et al. Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling. Environ Sci Pollut Res (2021).

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  • Groundwater resource
  • Machine learning (ML)
  • Statistical analysis
  • Sensitivity analysis
  • Petri net