WATER QUALITY MONITORING USING MACHINE LEARNING AND IOT: A REVIEW
Tahsin Fuad Hasan, Nassereldin Ahmed Kabashi, Tanveer Saleh, Md. Zahangir Alam, Mohd Firdaus Wahab, Abdurahman Hamid Nour
Abstract
Water remains one of the most essential natural resources. With the ever increasing population, the demand for water in various sectors, including agriculture, industry, power, as well as the prevelance of population. the avalaibility fresh and usable water is becoming limited, causing to significant strain on water supplies. Therefore, quality monitoring and analysis of water is of great importance to maintain for sustainable use and overall environmental protection. Traditional water quality monitoring techniques involve manual sampling, testing, and investigation, which in retrospect may not always be reliable and may be inefficient in advance warning of water quality detrioration. However, with the emergence of machine learning (ML) and Internet of Things (IoT) technologies, process of water quality monitoring and analysis have become more efficient, accurate, and cost-effective. ML algorithms are capable of analyzing large volumes of data on water quality, enabling the creation of data-centric approaches for designing, supervising, simulating, assessing, and refining different water treatment and management systems.This review paper provides an overview of the past and current application of machine learning and IoT in water quality monitoring and analysis. The paper consists and covers various algorithm within machine learning, such as supervised and unsupervised learning, deep learning, and the respective applications, as well as the use of IoT sensors for real-time monitoring of water quality parameters, such as pH, dissolved oxygen, temperature, and turbidity