Real Time Water Contamination Monitoring Using Digital Twin Approach
Keywords:
Digital Twin, Water Quality Monitoring, Raspberry Pi, pH Sensor, Turbidity Sensor, TDS Sensor, Temperature Sensor, SMS Alert, Real-Time Monitoring, Water Contamination DetectionAbstract
This Real-time monitoring of water quality is essential for protecting public health and ensuring safe water usage in domestic, industrial, and environmental applications. Conventional laboratory-based testing methods are often time-consuming, discontinuous, and unsuitable for instant contamination detection. To address these limitations, this work proposes a Real-Time Water Contamination Monitoring System Using a Digital Twin Approach. The system integrates a Raspberry Pi as the main processing unit with pH, turbidity, Total Dissolved Solids (TDS), and temperature sensors for continuous measurement of critical water quality parameters. A digital twin model is used to maintain synchronization between the physical monitoring unit and its virtual representation, enabling real-time analysis, status evaluation, and intelligent alert generation. In the proposed system, sensor data are acquired continuously and compared with predefined threshold values to determine whether the water condition is safe or abnormal. During experimental testing, the system successfully initialized with the LCD message “Data Reading Started” and continuously displayed live sensor outputs. Under normal conditions, the observed values were within acceptable ranges, approximately pH = 7.0–7.4, temperature = 27–30 °C, TDS = 280–420 ppm, and turbidity = 1.2–3.8 NTU, and the system classified the water as safe. When abnormal conditions were introduced, the parameters exceeded threshold limits such as pH < 6.5 or > 8.5, TDS > 500 ppm, turbidity > 5 NTU, and temperature > 35 °C, causing the LCD to display “Status: Abnormal”. The alert mechanism operated correctly during unsafe conditions. The red LED and buzzer were activated immediately, while safe water conditions were indicated through the green LED. In addition, the system generated an SMS notification with the message “Sending SMS Please Wait...”, confirming successful remote alert transmission to the user.
Downloads
References
R. Byrne, J. Diamond, and S. A. McCarthy, “Water quality monitoring using sensor-based systems: A review,” Environmental Monitoring and Assessment, vol. 190, no. 8, pp. 1–15, 2018.
M. S. Islam and T. A. Hasan, “Limitations of traditional water quality assessment techniques,” Journal of Water and Health, vol. 16, no. 4, pp. 567–580, 2018.
A. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
P. Kumar and U. R. Babu, “IoT-based real-time water quality monitoring system,” Procedia Computer Science, vol. 171, pp. 1980–1989, 2020.
S. Geetha and S. Gouthami, “Internet of Things enabled real-time water quality monitoring system,” Smart Water, vol. 2, no. 1, pp. 1–19, 2017.
M. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, and C. Youn, “Wearable 2.0: Enabling human–cloud integration in next generation healthcare systems,” IEEE Communications Magazine, vol. 55, no. 1, pp. 54–61, 2017.
K. Brindha and L. Elango, “Application of machine learning techniques for water quality assessment,” Applied Water Science, vol. 9, no. 3, pp. 1–11, 2019.
S. M. Zanetti, G. C. Mattos, and R. A. Braga, “Machine learning approaches for anomaly detection in water quality monitoring,” Water Resources Management, vol. 34, pp. 2301–2315, 2020.
F. Tao, Q. Qi, L. Wang, and A. Nee, “Digital twins and cyber–physical systems toward smart manufacturing,” Engineering, vol. 5, no. 4, pp. 653–661, 2019.
Y. Liu, X. Zhang, and C. Yang, “Digital twin-driven smart water management systems,” Journal of Cleaner Production, vol. 269, pp. 122–134, 2020.
Sepasgozar, S. Shirowzhan, and C. Wang, “Digital twin applications in infrastructure and environmental monitoring,” Automation in Construction, vol. 130, pp. 103–114, 2021.
A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital twin: Enabling technologies, challenges and open research,” IEEE Access, vol. 8, pp. 108952–108971, 2020.
H. Kaur and V. K. Jain, “Lightweight IoT and digital twin frameworks for smart environmental monitoring,” Sustainable Computing: Informatics and Systems, vol. 31, pp. 100–115, 2021.
E. Barricelli, E. Casiraghi, and D. Fogli, “A survey on digital twin: Definitions, characteristics, applications, and design implications,” IEEE Access, vol. 7, pp. 167653–167671, 2019.
J. Leng et al., “Digital twins-based smart manufacturing system design in Industry 4.0,” Journal of Manufacturing Systems, vol. 60, pp. 119–133, 2021.
S. Vijayakumar and S. Ramya, “The real time monitoring of water quality in IoT environment,” Procedia Computer Science, vol. 50, pp. 204–209, 2015.
A. Ahmed, M. A. Hossain, and A. Rahman, “Machine learning-based water quality prediction for smart water systems,” Water, vol. 12, no. 11, pp. 1–17, 2020.
M. K. Gayathri, J. Jayasakthi, and G. S. Anandha Mala, “Raspberry Pi based smart water quality monitoring system using machine learning,” International Journal of Engineering Research & Technology, vol. 9, no. 4, pp. 227–231, 2020.
S. Rathi and P. Gupta, “Embedded machine learning on Raspberry Pi for real-time environmental monitoring,” Sustainable Computing: Informatics and Systems, vol. 28, pp. 100–109, 2020.
A. Al-Fuqaha et al., “Edge computing enabled IoT-based water quality monitoring,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9872–9885, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0