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Water is supplied every day from a central reservoir by urban authorities in most of the urban areas. In water reservoirs and pipelines, water can be contaminated by pollutants in different ways. In city areas, people by consuming contaminated water, are susceptible to various diseases. In this work, our goal was to develop a system that would detect contamination, monitor, and control pipe water supply remotely. To achieve this goal, we have developed an Internet of Things (IoT) -based water quality detecting prototype utilizing Arduino and several sensors. These sensors can measure safe and risk-level values of water based on pH, temperature, turbidity, and total dissolved solids (TDS). If any of these parameter values cross the standard limit, then the water supply will automatically turn off and send an alert message using the global system for mobile communications (GSM) module to authorities and consumers. Moreover, users can monitor water quality in real-time on our designed website. So, consumers do not have a chance to drink contaminated water from the pipeline. To measure the proficiency of the developed system, a dataset containing 10, 000 data is created from the sensors, then labeled as safe or unsafe to drink based on WHO recommendations. The dataset was trained using different machine-learning algorithms. From our analysis, it is concluded that the decision tree algorithm achieved an accuracy of 99 \% on the proposed system. If the system is implemented practically in the water management system, we hope it will decrease many water diseases in a great number and overall service quality will be improved.
Dey et al. (Thu,) studied this question.