Reservoir water quality simulation with data mining models

Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE , NSE , RMSE , and R 2 goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R 2 = 0.97, NSE = 0.92) and the lowest values ( MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.