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In this study, the data at Idenak hydrometric station were used to predict the inflow to Maroun Dam reservoir. For this purpose, different models such as artificial intelligence, Holt-Winters and hybrid models were used. Partial mutual information algorithm was used to determine the input parameters affecting modeling the monthly inflow by artificial intelligence models. According to the Hempel and Akaike information criterion, we introduced the monthly inflow with a 3-month lag, and the temperature with a 1-month lag, with respect to the lowest values of Akaike and the highest values of Hempel as input parameters of artificial intelligence models. The results showed the weak performance of the Holt-Winters model compared to other models and confirmed the superiority of the Holt-adaptive network-based fuzzy inference system (ANFIS) hybrid model with the root-mean-square error of 54 and the coefficient of determination (R2) of 0.83 in the testing process compared to other mentioned models. In addition, the above hybrid models performed better than other models in the test process.

In this paper frequency analysis of annual extreme daily rainfall of 14 gauging stations located in an arid zone of Iran were performed using parametric and nonparametric approaches. The parametric methods include normal, two- and three-parameter log-normal, two-parameter gamma, Pearson and log-Pearson type III, extreme value type I (Gumbel), generalized extreme value and generalized logistic distributions. The nonparametric approach is Fourier series method. The data were fitted to all of above mentioned models and the results showed that the goodness of fit of the data to Fourier series is much better than other parametric methods. Thus the Fourier series can be used as an alternative approach for frequency analysis of extreme daily rainfall in an arid zone. In addition, the quantiles can be calculated by the Fourier series.

In this paper, annual precipitation data sets from five old rain gauge stations (Bushehr, Isfahan, Meshed, Tehran, and Jask) in Iran were fitted to nonparametric kernel function by using rectangular, triangular, and Gaussian or normal as kernel functions. The smoothing parameter was calculated by four methods including rule of thumb, Adamowski criterion, least squares cross-validation, and Sheater and Jones plug-in. The Adamowski criterion showed a better performance compared to other methods due to goodness of fit tests. The results of these proposed nonparametric methods will be then compared to the results of the parametric density functions including normal, two and three parameter log-normal, two parameter gamma, Pearson and log-Pearson type 3, Gumbel or extreme value type 1 and also Fourier series method which were applied by a previous study for the same stations. It was concluded that the annual precipitation data were fitted to nonparametric methods better than parametric methods.