Water Quality Evaluation and Monitoring Model (WQEM) Using Machine Learning Techniques with IoT

In present decade, because of fast growth of industries and rapid urbanization, the quality of natural water resources is deteriorated in higher rates, results in harmful and life-threatening diseases. Hence, there is a significant requirement in models for Water Quality Test and Analysis, which is time and cost effective. With that note, this research develops a novel model called Water Quality Evaluation and Monitoring Model (WQEM) that utilizes machine learning and Internet of Things (IOT) technique for performing water quality test. Moreover, Water Quality Index is estimated for providing the basic water qualities and based on that Quality Classification is also performed with the Multilayer Perceptron (MLP) based classification model. This work mainly focuses on the water fluoride content other factors such as Dissolved Oxygen, Total Dissolved Solids and pH. The experimentation is carried out using Raspberry Pi3 test kit and WEKA tool using the real-time data. The evaluation results evidence that the proposed model produces higher rate of classification results with minima Mean Absolute Error (MAE) than other compared water quality test models.