Application of artificial neural networks and mathematical modeling for the prediction of water quality variables (case study: southwest of Iran)

River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35years' data were collected from 7 monitoring stations and 5 rivers located in southwest of Iran. DO concentration was simulated using an equation and a neural model. Two equations were calibrated for estimating TDS while SAR is simulated using a mathematical and neural model. Another two neural models were developed for BOD5 and HCO3 simulations. An acceptable precision was achieved, as shown in model verification results. Presented models and equations are reliable/useable tools for studying in similar locations (rivers), as a proper replacement for traditional water quality measuring practices.