Access to safe drinking water is essential for human health, yet contamination and poor water quality remain major global concerns. This project presents an IoT Integrated Smart Drinking Water Quality Assessment and Potability Prediction System designed to continuously monitor water quality and predict its suitability for drinking. The system integrates multiple sensors including temperature, pH, turbidity, total dissolved solids (TDS), water level and Light intensity to measure key water quality parameters in real time. The sensor outputs are primarily analog signals, which are converted into digital data using the Analog-to-Serial conversion capability of the Arduino Nano’s built-in Analog-to-Digital Converter (ADC). The Arduino Nano collects and processes the sensor readings and communicates the data serially to a Raspberry Pi Zero 2W for advanced processing and cloud connectivity. The Raspberry Pi implements Artificial Intelligence (AI) and Machine Learning (ML) algorithms to analyze the collected sensor data and predict the potability status of the water based on trained models. The predicted results and real-time sensor readings are displayed locally on an LCD display for user monitoring. In cases where the measured parameters exceed safe thresholds, an abnormal condition alert is generated using a buzzer, providing an immediate warning to users. For remote monitoring and data storage, the system uploads the collected sensor data and prediction results to the ThingSpeak cloud platform, enabling real-time visualization, data logging, and further analysis through IoT connectivity. The integration of sensor-based monitoring, AI-driven prediction, and cloud-based data management ensures an efficient and intelligent solution for drinking water quality assessment. This system provides a cost-effective, real-time, and automated water monitoring solution, which can help improve public health by ensuring safe drinking water through continuous monitoring and predictive analytics.        

The main objectives of the project are:

  To monitor drinking water quality in real time using multiple sensors such as temperature, pH, turbidity, TDS, Water level and Light intensity.

  To collect and process sensor data using Arduino Nano, where analog signals from sensors are converted into digital values using the built-in Analog-to-Digital Converter (ADC).

  To transmit sensor data to Raspberry Pi Zero 2W through serial communication for advanced processing and system control.

  To implement Artificial Intelligence (AI) and Machine Learning (ML) algorithms on the Raspberry Pi to analyze sensor data and predict whether the water is potable or not.

  To provide real-time status monitoring by displaying sensor readings, prediction results, and water quality status on an LCD display.

  To generate an alert during abnormal conditions by activating a buzzer when water parameters exceed safe limits.

  To upload sensor data and prediction results to the ThingSpeak cloud platform for remote monitoring, data visualization, and storage.

  To develop a low-cost and efficient IoT-based smart water monitoring system that helps ensure safe drinking water and improves public health.        

The major building blocks of this project are:  

• Power supply.
• Arduino Nano.
• Raspberry pi zero 2w.
• SD card.
• PH sensor.
• Temperature sensor.
• Turbidity
• TDS sensor.
• Water level and Light intensity.
• LCD display.
• Buzzer.

      Software’s used:  

• Arduino IDE for compiling and dumping code into Microcontroller.
• Raspbian OS.
• Thingspeak Cloud.
• Express SCH for Circuit design.
• AI and Machine learning algorithm.

       

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