Modeling of Water Quality Parameters Using Data-Driven Models

Abstract

Water has a considerable role in all aspects of human life. Thus, evaluation of water characteristics in general and water quality in particular are necessary to enhance the health of humans and ecosystems. Data-driven models are computing methods that are capable of extracting different system states without using complex relationships. Prediction and simulation are two branches of data-driven modeling that use previous and previous-current data sets to fill gaps in time series. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two data-driven models to predict and simulate water quality parameters (e.g., sodium, potassium, magnesium, sulfates, chloride, pH, electrical conductivity, and total dissolved solids) at the Astane station in Sefidrood River, Iran. The writers considered six combinations of data sets, including the previously noted water quality parameters and river discharge in the previous and previous-current months, as input data. Implementation of the ANFIS and GP models in this paper illustrates the flexibility of GP in time series modeling relative to ANFIS, especially in the testing data set. Accordingly, the writers calculated the coefficient of variation of root mean squared error as the error criterion in different ANFIS and GP models (for assigning achievement probability to an appropriate solution) for each quality parameter. The average of the previously noted values for the six combinations of data sets improved (decreased) 80.51 and 80.89%, respectively, in the training and testing data sets with GP relative to ANFIS. These results indicate that the writers’ proposed modeling, based on GP, is an effective tool for determining water quality parameters.