Drinking Water Quality Analysis and Prediction Using LSTM: Safe Drinking Water for School Children

Ensuring safe drinking water is vital for human health, but it remains a challenging task. School children are vulnerable to various diseases caused by contaminated water. To address this issue, this study proposes a system that utilizes machine learning and IoT to provide a constant supply of pure drinking water for school children. The system collects water parameter data using different sensors, including pH, DS18B20 temperature, and turbidity sensors. The collected data is transmitted to the cloud via an Arduino UNO and ESP 8266 Wi-Fi protocol. The Long Short-Term Memory (LSTM) algorithm is then applied to analyze the data and predict water quality levels. The system's performance was evaluated through several test cases, and the results were promising. The IoT hardware successfully transmitted real-time sensor readings to the machine learning model through the cloud, ensuring drinking water quality. The LSTM model achieved an average 93.33% R2 score and performed real-time forecasts. Overall, the proposed system can help ensure safe drinking water for school children by leveraging the benefits of machine learning and IoT.