Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Abstract

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.