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Surface water pollution monitoring using the Internet of Things (IoT) and machine learning

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Water is one of the basic resources required for human survival. However, pollution of water has become a global problem. 2.4 billion people worldwide live without any form of water sanitation. This work focuses on case study of water pollution in Pakistan where only 20% of the population has an access to good-quality water. Drinking bad-quality water causes diseases such as hepatitis, diarrhea and typhoid. Moreover, people living close to the industrial areas are more prone to drinking polluted water and catching diseases as a result. Yet, there is no system that can monitor the quality of water or help in disease prevention. In this work, an Internet of Things (IoT)-enabled water quality monitoring system is developed that works as a stand-alone portable solution for monitoring water quality accurately and in real time. The real-time results are stored in a cloud database. The public web portal shows these results in the form of data sheets, maps and charts for analyzing data. Further, this data along with the collected data of past water quality is used to generate machine learning (ML) models for prediction of water quality. As a consequence, a model for prediction of water quality is trained and tested on a test set. The predictions on the test set resulted in a mean squared error (MSE) of 0.264.

Chapter Contents:

• 17.1 Introduction
• 17.2 Literature review
• 17.3 Methodology
• 17.3.1 Development of water quality monitoring IoT nodes
• 17.3.1.1 Selected parameters
• 17.3.1.2 Sensors
• 17.3.1.3 Temperature sensor
• 17.3.1.4 Dissolved oxygen sensor
• 17.3.1.5 Conductivity sensor
• 17.3.1.6 pH sensor
• 17.3.1.7 Turbidity sensor
• 17.3.1.8 Interfacing sensors
• 17.3.1.9 Design of water quality monitoring IoT nodes
• 17.3.1.10 Sensors calibration
• 17.3.2 Development of wireless sensor network
• 17.3.2.1 Cloud back end
• 17.3.3 Data visualization
• 17.3.3.1 Web portal development
• 17.3.4 Prediction of water quality using machine learning
• 17.4 Results and discussion
• 17.5 Conclusion and future work
• Acknowledgment
• References

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