From Dam to Tap in Mumbai: Real- Time Water Quality Monitoring using IOT and Machine Learning - Pearl Dsouza; Namrata Joshi; Noelle Shaji; Prof. Prachi Patil; Prof. Monali Shetty (The Bioscan, 2025)
This research explores water supply from dams to households at real-time monitoring of the quality of the water with IoT sensors and subsequent machine learning. It ensures that water supplied from dams to reservoirs and further to household service remains pure even after its supply. The methodology includes the collection of water at the Bhandup Complex for purification and its subsequent distribution to 26 service reservoirs throughout Mumbai. For training and testing different machine learning algorithms, namely SVM, Random Forest, and LightGBM, data from these reservoirs and purified water in the Bhandup Complex are used. LightGBM achieved high accuracy on the other algorithms and achieved an accuracy of 97.5% on purified water and 75.5% on the corresponding reservoir water. Real-time monitoring of water quality by IoT sensors in houses can give information on pH, turbidity, TDS, and many other parameters. It can help trace out specific areas of contamination in the water distribution network further. Major Findings for the present study comprised the reliability of AquaSage to predict water quality and the presence of potential points of pollution, thereby ensuring that the water supply from the treatment plant would be safe for consumption. This technology brings to light the major life improvements for the citizens of public health through prompt intervention and the provision of timely water quality. Thanks to their features, one of which is the possibility of an early stage of knowing that contamination exists, they are also cutting the risk of waterborne diseases by limiting the growth of bacteria and such, so they are a great way to help sustainable water management also. On the other hand, the research shows some limitations. The data for dams and reservoirs, based on BMC's provided ranges, covers only five years and is static, not real-time. Efficiency of hardware sensors may decrease over time, and the newest scaling is possible only at households, but not in society or at building level. With this in mind, the major benefits of the study come in terms of creating an IoT infused machine learning algorithm for monitoring the projects in real-time and therefore using a user-friendly system that empowers citizens to ensure water safety. Practical implications involve eliminating the threats of waterborne illnesses in the city through giving the city dwellers an honest mechanism to avail clean water supply. The real-time monitoring features of AquaSage also prove useful for precautionary measures against contamination and sustainable water management. Later studies should focus on refining precision in forecasting, additional sensor integration, extended performance testing, user interface optimization, and use cases in aquaculture and industrial on-site water quality monitoring. More scope for the improvement of water treatment processes include full system automation and integration into water purification efforts.