Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Abstract

Careful monitoring of the quality and quantity of water resources plays a significant role in modern environmental management. Thus, to achieve this aim, the quantitative and qualitative parameters of water resources should be measured and controlled with a desirable accuracy. However, it is not always possible to measure these parameters with easy, inexpensive, precise, and quick experimental methods. Therefore, today to solve this type of problems, new methods including smart methods are used which have a great potential in many computational areas. Considering the variety of the studies and absence of a comprehensive review paper, this research should be conducted. The aim of this paper is to comprehensively review application of the smart methods of artificial neural network and support vector machine in the area of water resources and to develop a comprehensive study source for the researchers interested in this field. The results of these studies all show that these advanced smart methods are more efficient, accurate, economical, and faster than other computational methods to predict quantitative and qualitative parameters of water resources.

Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants

“…In addition, the data to be used in the development of intelligent systems need to contain all existing scenarios so that the intelligent prediction model can assist with all possible operational conditions [20,26,27]. An alternative used in the identification of similar operational conditions is the submission of a database to processing using clustering techniques, which has the purpose of identifying different operational scenarios with their respective records [28][29][30].…”