Smart Platform for Water Quality Monitoring System using Embedded Sensor with GSM Technology

Abstract

Point and non -point sources Surface water pollution causes damaging effects to the environment, aquatic life and human health. Thirty existing water monitoring stations use Industrial Revolution (IR) 3.0 technology with limited access to the public. A multi-sensory hub with Internet-of-Things (IoT) functionality can be developed to monitor river health and data collected continuously through the Global System for Mobile Communications (GSM) and cloud systems that inform locals about water quality index (WQ) for reducing the likelihood of early. harmful effects produced from oil palm plantations in Sungai Semborong, Batu Pahat, Johor. The inclusion of machine learning into the system will help classify and estimate pollutant types. The potential for pollution reduction using Algae balls is proposed and will be investigated. The methodology that will be used in this research is the Design and development Research (DDR) study. The objective is to design and develop a GSM cloud-based multi-sensor system to monitor river health related parameters such as dissolved electrical conductivity (EC), acidity (pH), dissolved oxygen (DO) and oxygen reduction potential (ORP) b) to develop modeling solid real -time WQ forecasts. Expected discoveries are an IoT -based multi-sensor system with machine learning data analysis capabilities, a new machine learning model for pollution estimation and the discovery of effective methods to treat polluted water using green technology. As such, the project addresses two sustainable development (SDG) objectives, to provide clean water and protect underwater life as gazetted in the national water resources policy.