Efficient IOT based Water Quality Prediction Using Cat Swarm Optimized Neural Network classification

Abstract

Water is the most significant sources for human life, but, it is in serious threat of contamination by life itself. The protection and availability of drinking-water are major worries throughout the globe. In this work,anIOT based solution isintroduced to check and predict the water quality and alert the user before the water gets polluted. The proposed system uses IoT and optimized neural network for prediction. It consists of various embedded sensors like conductivity, pH, turbidity and color.  The measured sensor values are stored in the database and further directed for prediction analysis. The Cat swarm optimization (CSO) based neural network algorithm is used for forecasting thequality result.  The proposed system alerts the user when any of themeasured parameters are lesser than the fixed thresholds. This technique can also be implemented in water plants, rivers and industries.