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Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning
Summary
Researchers developed a cloud-based smart water quality monitoring system using IoT sensors and machine learning to detect contamination parameters such as pH, nitrate, conductivity, and fecal coliform in real time. The system applies machine learning classification to correlated sensor data to enable early detection of health hazards from contaminated water sources.
Low water quality is a major concern in urban as well as rural areas. Consumption of contaminated water leads to several health hazards. Early water quality detection can prevent most of such health-related issues. Parameters such as conductivity, pH, nitrate, biochemical oxygen demand, fecal coliform are significant parameters in deciding the quality of water. These parameters which are collected from groundwater samples at different places are highly correlated to each other. Therefore, machine learning algorithms are used for classification. The data collected from sensors are further analyzed using a cloud-based environment Ubidots to support distributed computing. The cloud environment is connected to display units and mobile devices. To predict the quality of water it is necessary to check the values associated with the quality attributes and for that reason, a decision tree classification model is used. The dataset is broken into subsets that have decision nodes and leaf nodes to decide classifications. The IoT based sensors are deployed in the water tank to measure the quality parameters which are further sent to the cloud. The proposed framework predicts the water quality and assesses the performance of the decision tree classifier. Decision Tree is used to infer decision rules based on various parameters read through sensors.
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