Machine Learning Based Realtime Water Quality Monitoring System

Abstract

Water quality parameter is of much importance in our day to day lives. Prediction of water quality will help to reduce water pollution and guard our human health. This work has advanced an “Machine Learning based real-time water quality monitoring system” pertaining to lakes is being used in rural areas. All the organisms that use it are affected by the waste generated in this water. Water quality monitoring system is to identify the level of water and finding ways to correct the problems in it. Water quality refers to the chemical, physical and biological characteristics of water. It is a measure of the condition of water relative to the requirements of one or more biotic species and or to any human need or purpose. It is most frequently used by reference to a set of standards against which compliance can be assessed. The most common standards used to assess water quality relate to health of ecosystems, safety of human contact and drinking water. An intelligent process of monitoring the quality of water automatically detects the condition of water through Machine Learning by processing sensors data and instantly provides notification to water analyst when the quality of water is abnormal. The structure uses Determination of pH, Color, Temperature, Carbon monoxide, Conductivity, Fecal coliform. Likewise, ANN, Adaptive filter algorithm, Support Vector Machine, Naïve Bayes, Random forest calculation has been utilized for anticipating the nature of water, with the assistance of prepared informational collection from various water tests.