Machine learning model for IoT-Edge device based Water Quality Monitoring

The aim of this work is to intelligently detect alarming events in the water quality using machine learning techniques at the edge device, which is adaptive to localities, applications and also time. There are four objectives of this work; (1) To develop an edge device for sensing the water quality parameters (2) to detect changes in the water quality with respect to base line parameter using a machine learning approach at the edge device itself (3) to generate the alarm signals when water quality parameters go beyond its threshold value and (3) to classify different types of contamination and analyze them for identifying possible contamination types. For the experimentation, three water quality indicative methods are used to calculate the water quality, namely (a) Weighted Arithmetic Index, (b) NSF Water Quality Index and (c) User feedback of the water quality. Water quality is determined using water quality indexes (WQI) on the basis of six physico-chemical sensor parameters like biological oxygen demand, dissolved oxygen, pH, total hardness, total dissolved solids and turbidity. With the help of WQI of these methods, a light weight machine learning model which is suitable for the edge device, has been developed using the Support Vector Machine (SVM) algorithm. We also clustered the alarming events to find out different types of alarming events.