Unlocking IoT and Machine Learning’s Potential for Water Quality Assessment: An Extensive Analysis and Future Directions

Managing water quality is one of the most specific issues facing humanity today. The present research examines the different sensors that are utilized to monitor the quality of water, with a particular emphasis on the water quality index, taking into account a number of chemical, biological, and physical aspects. An overview of IoT studies on detectors for analysis and monitoring of water quality shows that these kinds of sensors can assist with distant surveillance of parameters related to water quality by employing a variety of sensors based on the IoT that transmit the combined estimates through low-power WAN developments. For assessing turbidity, pH, TDS, and temperature, the IoT approach proved 95.00% accurate in general, compared to 85.00% accuracy for the conventional approach. Additionally, the present research examined the various artificial intelligence methods—such as DNN, KNN, SVM, and conventional machine learning methods—that are employed to estimate the quality of water. The accuracy of groundwater quality measurements can be greatly improved over conventional techniques by utilizing deep learning and machine learning. The accuracy will be impacted by a number of factors, though, including the complexity of the water quality metrics, the frequency of monitoring, and the training data quality. Analyzing spatial statistics and organizing water assets are two uses for the geographical information system (GIS). In the article, the data quality is also evaluated. The study’s predictions for geospatial technologies, future sensors, and machine learning methods for analyzing and monitoring water quality are based on these analyses.