PREDICTIVE MODELS FOR RIVER WATER QUALITY USING MACHINE LEARNING AND BIG DATA TECHNIQUES - A SURVEY

Abstract

Water is an important and essential element for the life on earth. Due to the growth of population and industrialization the water resources become more polluted. Waste disposal from industry, human wastes, automobile wastes, agricultural runoff from farmlands containing chemical factors, unwanted nutrients, and other wastes from point and non-point source flow to water bodies, which affects the quality of the water resources. etc. The increase in pollution influences the quantity and quality of water, which results high risk on health and other issues for human as well as for living organisms on the planet. Hence, evaluating and monitoring the quality of water, and its prediction become crucial and applicable area for research in the current scenario. In various researchers they have used traditional approaches; Now, they are using technologies like machine learning, big data analytics for evaluation and prediction of water quality. The advanced big data implementation using sensor networks and machine learning with the data related to environment, aids in building water quality prediction models. This paper analyses various prediction models developed using machine learning and big data techniques and their experimental results of water prediction and evaluation. Various challenges and issues are reviewed and possible solutions to some research issues are proposed.