Following the extraction of constant winds, 1,200 hourly data were selected, of which, 1,080 data points (the data set of year 2001) were used as the training data set and the remaining data (data set of year 2013) were selected as the testing data set. Selection of another year for the testing is to provide the fair evaluation of the developed model in a different climate. Out of 1,080 data points, 800 data points were chosen as the training data points and the remaining 220 data points used as the validation data to avoid overtraining of the model. Statistical characteristics such as the minimum, maximum, average and range of all data points are reported in Table 1. In the selected intervals of the data set, the maximum and minimum of the recorded wind speed are 16.52 m/s and 5.75 m/s, respectively. This proves that the data set covers a wide range of wind climate at the lake. These data have been selected among a total of 4,554 hourly data, in which data points with wave height less than 0.5 m (the common calm condition in maritime design) have been eliminated from the data set. In addition, existing gaps in the data set either have been excluded from the data set or interpolated. Also, wind speeds have been converted to 10 m above sea level wind speeds. In order to evaluate the performance of the combined FIS and GA model in this section, the GA model is applied in three states. The first state of the GA model application goes back to its application for optimizing the subtractive clustering parameters, i.e., radii of inputs and output variables and the quash factor, leading to extraction of fuzzy IF-THEN rules. In the second state, the GA is employed only to tune the antecedent and consequent parameters of the resultant fuzzy IF-THEN rules from the first step. In the third state, the GA model is used for simultaneous optimization of the subtractive clustering parameters and the antecedent and consequent parameters associated with the selected fuzzy IF-THEN rules from clustering parameters. Note that in this form of the GA application for optimizing fuzzy IF-THEN rules, the number of the antecedent and consequent parameters are related to subtractive clustering parameters. Therefore, the number of the decision variables changes during the execution of the GA model.