The paper "Smart Water Quality Monitoring with IoT Wireless Sensor Networks" by Yurav Singh and Tom Walingo discusses the evolution of water quality monitoring from traditional laboratory-based methods to more advanced wireless sensor network (WSN)-based techniques. The authors highlight the limitations of traditional methods, such as the need for real-time feedback and immediate responses to emergencies, and how WSNs can address these issues. The integration of the Internet of Things (IoT) into WSNs further enhances their capabilities, but they still face challenges like inaccurate real-time data, lack of reconfigurability, and limited industry acceptance. The paper introduces a smart water-quality-monitoring framework that utilizes artificial intelligence (AI) sensors to predict water quality indicators, particularly E. coli concentrations, when electronic sensors are not available. Machine learning algorithms, including ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor, are used to predict E. coli concentrations based on a dataset collected from four water-treatment plants in South Africa over seven years. The study found that the AdaBoost regressor performed the best in predicting E. coli concentrations, with an average mean absolute error (MAE) of 14.37 counts/100 mL, while the stochastic gradient boosting performed the worst with an average MAE of 42.27 counts/100 mL. The research also highlights the importance of using commonly available and affordable wireless sensors for initial deployment to improve model performance, although adding more advanced sensors later can further enhance predictions. The paper concludes by discussing the challenges and future directions for smart water-quality monitoring, emphasizing the need for better communication technologies, energy efficiency, security, and sensor reliability in underwater environments.The paper "Smart Water Quality Monitoring with IoT Wireless Sensor Networks" by Yurav Singh and Tom Walingo discusses the evolution of water quality monitoring from traditional laboratory-based methods to more advanced wireless sensor network (WSN)-based techniques. The authors highlight the limitations of traditional methods, such as the need for real-time feedback and immediate responses to emergencies, and how WSNs can address these issues. The integration of the Internet of Things (IoT) into WSNs further enhances their capabilities, but they still face challenges like inaccurate real-time data, lack of reconfigurability, and limited industry acceptance. The paper introduces a smart water-quality-monitoring framework that utilizes artificial intelligence (AI) sensors to predict water quality indicators, particularly E. coli concentrations, when electronic sensors are not available. Machine learning algorithms, including ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor, are used to predict E. coli concentrations based on a dataset collected from four water-treatment plants in South Africa over seven years. The study found that the AdaBoost regressor performed the best in predicting E. coli concentrations, with an average mean absolute error (MAE) of 14.37 counts/100 mL, while the stochastic gradient boosting performed the worst with an average MAE of 42.27 counts/100 mL. The research also highlights the importance of using commonly available and affordable wireless sensors for initial deployment to improve model performance, although adding more advanced sensors later can further enhance predictions. The paper concludes by discussing the challenges and future directions for smart water-quality monitoring, emphasizing the need for better communication technologies, energy efficiency, security, and sensor reliability in underwater environments.