Authors : Atharva Aminbhavi; Yash Nagrale; Sandhya Jaiswal; Aditi Jha; Dr. Pratibha Adkar

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/m579wyhe

DOI : https://doi.org/10.38124/ijisrt/26Oct879

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Water quality monitoring is important for identifying changes in water conditions and providing timely warnings when unusual measurements occur. Conventional laboratory testing provides accurate results but is not suitable for continuous real-time monitoring. This project proposes an AI- and IoT-based Water Quality Early Warning System that combines water-quality sensors, embedded data acquisition, wireless communication, backend processing, machine learning, and a web dashboard. The system collects sensor readings, validates and processes the data, detects abnormal behaviour, and presents the results through a monitoring interface. The architecture also provides a framework for future pH estimation and water-quality classification using machine-learning models. Since pH is not directly measured in the current hardware, any estimated value must be validated against actual pH measurements before reliable use. The project includes temperature, TDS, and turbidity sensors connected initially to an Arduino Uno R3. An ADS1115 ADC can be used for higher-resolution analog measurements, while an ESP8266 NodeMCU V3 can provide Wi-Fi-based communication. The backend uses Python and FastAPI for data processing, with MQTT proposed for sensor-data transmission. A web dashboard displays live readings, real-time graphs, connection status, range information, and recent readings. The system also provides scope for sensor anomaly detection, pH estimation, and early-warning classification using AI/ML techniques.

Keywords : Real-Time Monitoring, MQTT Communication, Sensor Fault Detection, Early Warning System, ESP8266, TDS, Turbidity.

References :

1. S. Sharma, D. Mishra, A. Yadav, B. Gami, et al., “An Intelligent, Low-Cost Water Quality Monitoring System with On-Device Machine Learning and Cloud Integration,” Scientific Reports, Vol. 16, Article No. 11106 (2026).
2. S. Kushwaha and R. Pandey, “Intelligent IoT-Based Water Quality Monitoring and Predictive Analysis Using Machine Learning,” AI Systems Engineering, Vol. 1, No. 2, pp. 8–16 (2025).
3. D. Ishikura, H. Hara-Yamamura, R. Igarashi, K. Otani, S. E. Yola, S. Hanamoto, R. Yamamoto-Ikemoto, H. Yamamura, R. Honda, “Fluorescent fingerprints-based anomaly detection in drinking water quality and identification of contributing features by explainable AI,” Discover Water, Vol. 5, Issue 1, Article 116 (2025).
4. Y. Nie, K. H. Yu, C. P. Leo, K. S. Yen, H.-B. Yang, and T. Matsumura, “Machine learning-based calibration of IoT turbidity sensors: enhancing sensing accuracy and implementation,” Engineering Research Express, Vol. 7, No. 4, Article 0455 (2025).
5. A. Aderemi, T. O. Kehinde, U. D. Okwor, K. H. Ahmad, K. Y. Adjei, and C. C. Ekechi, “Explainable AI for Water Quality Monitoring: A Systematic Review of Transparency, Interpretability, and Trust,” IEEE Sensors Reviews, Vol. 2, pp. 419–443 (2025).
6. S. Kushwaha and R. Pandey, “Intelligent IoT-Based Water Quality Monitoring and Predictive Analysis Using Machine Learning,” AI Systems Engineering, Vol. 1, No. 2, pp. 8–16 (2025).
7. M. S. B. Shahid, H. R. Rifat, M. A. Uddin, M. Islam, M. Z. Mahmud, M. K. H. Sakib, and A. Roy, “Hypertuning-Based Ensemble Machine Learning Approach for Real-Time Water Quality Monitoring and Prediction,” Applied Sciences, Vol. 14, Issue 19, Article 8622 (2024).
8. Saeed, A. Alsini, and D. Amin, “Water Quality Multivariate Forecasting Using Deep Learning in a West Australian Estuary,” Environmental Modelling & Software, Vol. 171, Article 105884 (2024).
9. H. Jeong, S. Park, B. Choi, C. S. Yu, J. Y. Hong, T.-Y. Jeong, and K. H. Cho, “Machine Learning-Based Water Quality Prediction Using Octennial In-Situ Daphnia magna Biological Early Warning System Data,” Journal of Hazardous Materials, Vol. 465, Article 133196 (2024).
10. V. Urbanovičs, S. Parshutin, J. Rubulis, M. Bonders, K. Dambeniece, R. Ozols, D. Štēbelis, and S. Dejus, “Performance Evaluation of Machine Learning Methods for Drinking Water Contamination Detection,” Engineering Proceedings, Vol. 69, Article 110 (2024).
11. M. A. López-Munoz, R. Torrealba-Melendez, C. A. Arriaga-Arriaga, E. I. Tamariz-Flores, M. López-López, F. Quirino-Morales, J. M. Munoz-Pacheco, and F. López-Marcos, “Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT,” Technologies, Vol. 12, Article 211 (2024).
12. L. M. Pires and J. Gomes, “River Water Quality Monitoring Using LoRa-Based IoT,” Designs, Vol. 8, Article 127 (2024).
13. W. A. Jabbar, T. M. Ting, M. F. I. Hamidun, A. H. Che Kamarudin, W. Wu, J. Sultan, A. A. Alsewari, and M. A. H. Ali, “Development of LoRaWAN-based IoT system for water quality monitoring in rural areas,” Expert Systems With Applications, Vol. 242, Article 122862 (2024).
14. M. Naloufi, T. Abreu, S. Souihi, C. Therial, N. A. de Ponte Rodrigues, A. Guillot Le Goff, M. Saad, B. Vinçon-Leite, P. Dubois, M. Delarbre, P. Kennouche, and F. S. Lucas, “Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers,” Water, Vol. 16, Issue 12, Article 1708 (2024).
15. K. Lal, S. Menon, F. Noble, K. M. Arif, “Low-cost IoT based system for lake water quality monitoring,” PLOS ONE, Vol. 19, Issue 3, Article e0299089 (2024).
16. Z. Czyczula Rudjord, M. J. Reid, C. U. Schwermer, and Y. Lin, “Laboratory Development of an AI System for the Real-Time Monitoring of Water Quality and Detection of Anomalies Arising from Chemical Contamination,” Water, Vol. 14, Issue 16, Article 2588 (2022).
17. N. Mokua, C. wa Maina, and H. Kiragu, “Anomaly Detection for Raw Water Quality – A Comparative Analysis of the Local Outlier Factor Algorithm and the Random Forest Algorithms,” International Journal of Computer Applications, Vol. 174, No. 26, pp. 47–54 (2021).
18. V. Meleshko, V. A. Desnitsky, and I. V. Kotenko, “Machine learning based approach to detection of anomalous data from sensors in cyber-physical water supply systems,” IOP Conference Series: Materials Science and Engineering, Vol. 709, No. 3, Article 033034 (2020).
19. F. Muharemi, D. Logofătu, and F. Leon, “Machine learning approaches for anomaly detection of water quality on a real-world data set,” Journal of Information and Telecommunication, Vol. 3, No. 3, pp. 294–307 (2019).
20. P. Rathore, D. Kumar, S. Rajasegarar, and M. Palaniswami, “Maximum Entropy-Based Auto Drift Correction Using High- and Low-Precision Sensors,” ACM Transactions on Sensor Networks, Vol. 13, No. 3, Article 24 (2017).

• Technical Web Resources

21. https://www.php.net/
22. https://www.mysql.com/
23. https://www.apachefriends.org/
24. https://developer.mozilla.org/
25. https://getbootstrap.com/
26. https://cloud.google.com/vision
27. https://ocr.space/
28. https://openai.com/
29. https://www.nhp.gov.in/
30. https://www.pharmaceuticaltechnology.com/
31. https://www.w3schools.com/
32. https://www.geeksforgeeks.org/
33. https://www.tutorialspoint.com/