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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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.
Alpaydin E (2010) Introduction to machine learning, Second edn. The MIT Press
Band SS, Janizadeh S, Pal SC, Chowdhuri I, Siabi Z, Norouzi A, Melesse AM, Shokri M, Mosavi A (2020) Comparative analysis of artificial intelligence models for accurate estimation of groundwater nitrate concentration. Sensors 20(20):5763
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC press
Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28(13):4747–4763
Candelieri A, Soldi D, Archetti F (2015) Short-term forecasting of hourly water consumption by using automatic metering readers data. Procedia Engineering 119(1):844–853
Chen W, Li Y, Tsangaratos P, Shahabi H, Ilia I, Xue W, Bian H (2020) Groundwater spring potential mapping using artificial intelligence approach based on kernel logistic regression, random forest, and alternating decision tree models. Appl Sci 10(2):425
Coalition TB (2016) The digitization of sanitation. Pune: Toilet Board Coalition) Retrieved November 12:2019
Dawidowicz J (2018) Evaluation of a pressure head and pressure zones in water distribution systems by artificial neural networks. Neural Comput & Applic 30(8):2531–2538
Fathollahi-Fard AM, Ahmadi A, Al-e-Hashem SM (2020a) Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. J Environ Manag 275:111277
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tian G, Li Z (2020b) An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem. Inf Sci 512:1335–1359
Gheibi M, Karrabi M, Eftekhari M (2019) Designing a smart risk analysis method for gas chlorination units of water treatment plants with combination of Failure Mode Effects Analysis, Shannon Entropy, and Petri Net Modeling. Ecotoxicol Environ Saf 171:600–608
Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75(6):476
Guzman SM, Paz JO, Tagert MLM, Mercer A Artificial neural networks and support vector machines: contrast study for groundwater level prediction. In: 2015 ASABE annual international meeting, 2015. American Society of Agricultural and Biological Engineers, p 1
Haghiabi AH, Nasrolahi AH, Parsaie A (2018) Water quality prediction using machine learning methods. Water Quality Research Journal 53(1):3–13
Hanswal P, et al. (2013) Designing a central control unit and soil moisture sensor based irrigation water pump system. Texas Instruments India Educators’ Conference
Hering JG, Waite TD, Luthy RG, Drewes JE, Sedlak DL (2013) A Changing Framework for Urban Water Systems. In: A changing framework for urban water systems. ACS Publications
Hill D, Kerkez B, Rasekh A, Ostfeld A, Minsker B, Banks MK (2014) Sensing and cyberinfrastructure for smarter water management: the promise and challenge of ubiquity. American Society of Civil Engineers
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Ingildsen P, Olsson G (2016) Smart water utilities: complexity made simple. IWA Publishing
Kiparsky M, Sedlak DL, Thompson BH Jr, Truffer B (2013) The innovation deficit in urban water: the need for an integrated perspective on institutions, organizations, and technology. Environ Eng Sci 30(8):395–408
Kombo OH, Kumaran S, Sheikh YH, Bovim A, Jayavel K (2020) Long-term groundwater level prediction model based on hybrid KNN-RF technique. Hydrology 7(3):59
Ma Z, Wang S (2009) Energy efficient control of variable speed pumps in complex building central air-conditioning systems. Energy and Buildings 41(2):197–205
Markard J, Raven R, Truffer B (2012) Sustainability transitions: an emerging field of research and its prospects. Res Policy 41(6):955–967
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5(4):115–133
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, vol 821. John Wiley & Sons
Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Mollaefar E, Tiefenbacher J, Cipullo S, Ahmad BB, Tien Bui D (2019) Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and uneec methods. Sci Total Environ 688:855–866
Rhys HI (2020) Machine learning with R, the tidyverse, and mlr. Manning Publications
Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M (2015) Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818
Rodriguez-Galiano V, Luque-Espinar J, Chica-Olmo M, Mendes M (2018) Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci Total Environ 624:661–672
Schölkopf B, Bartlett P, Smola A, Williamson R Support vector regression with automatic accuracy control. In: International conference on artificial neural networks, 1998. Springer, p 111–116
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576
Thompson K, Kadiyala R (2014) Making water systems smarter using M2M technology. Procedia Engineering 89:437–443
Truffer B, Binz C, Gebauer H, Störmer E (2013) Market success of on-site treatment: a systemic innovation problem. IWA Publishing, London, UK, pp 209–223
Yoon H, Hyun Y, Ha K, Lee K-K, Kim G-B (2016) A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 90:144–155
Yoon H, Kim Y, Ha K, Lee S-H, Kim G-P (2017) Comparative evaluation of ANN-and SVM-time series models for predicting freshwater-saltwater interface fluctuations. Water 9(5):323
Ziyaee M (2018) Assessment of urban identity through a matrix of cultural landscapes. Cities 74:21–31
فاضلاب دتموکسهشآو firstname.lastname@example.org
In this paper are financial issues and requirements are supplied by the International University of Imam Reza.
Consent to participate
All authors have the same participation in this paper.
Consent to publish
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.
The authors declare no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Responsible Editor: Philippe Garrigues
About this article
Cite this article
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). https://doi.org/10.1007/s11356-021-12643-0
- Groundwater resource
- Machine learning (ML)
- Statistical analysis
- Sensitivity analysis
- Petri net