Prediction and Prevention of Water Pollution by Industries using IoT based Monitoring System

Abstract

The ever-increasing water pollution level due to industries, pose a serious threat to the masses and once detected, it requires a significant amount of time for the remedial measures. The proposed IoT solution discusses a device, capable of determining the water pollution level caused by an industry, near water bodies. The system would also be able to predict when the pollution level tends to cross the threshold value set by C.P.C.B. India in future. This information would eventually help in taking proactive remedial measures.The proposed device consists of four major sensors: pH sensor, turbidity sensor, flow sensor, and temperature sensor. These sensors would measure the water-parameters in real-time and send that data to Google Firebase (database) either through the WiFi module or, through the LoRa transmitter installed in the proposed device. Through LoRaWAN, all of these devices, which will be put at various industry-wastewater-outlets, will be wirelessly connected to a central hub. The goal is to use an optimized algorithm to make the system more energy efficient. The database would retain real-time data for two purposes: displaying it in the mobile app and using it in LSTM time-series forecasting (Machine Learning model) to predict when pollution levels could surpass the threshold.