Evaluation of the Effect of Probability Distributions on Suspended Sediment Prediction Accuracy using ANN and ANFIS Models (Case Study: Dez Basin)

Due to the nature of the sediment data, selection of appropriate methods for processing the data before entering them to the artificial intelligence models can enhance the reliability of simulations results. In this study, the effects of sediment data processing procedures on ANN and ANFIS models outputs in 7 Dez Basin stations were evaluated. Accordingly, three scenarios were considered: In the first scenario, original data was used without exerting any processing technique; in the second scenario, the data was normalized; and in the third scenario, logarithm of data were used according to logarithmic distribution governing. The simulation results showed that using data logarithm leads to higher performance and lower error, especially in stations where the best fit probability distribution is one of the log family distributions. Finally, among applied models, ANFIS showed the best performance with coefficient efficiency of 0.95 and RMSE of 5.4, MSE of 1.4 and ME of 0.42 in Biatoon gauging station and using the third scenario.