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Since wave energy has the highest marine energy density in the coastal areas, assessment of its potential is of great importance. Furthermore, long term variation of wave power must be studied to ensure the availability of stable wave energy. In this paper, wave energy potential is assessed along the southern coasts of Iran, the Persian Gulf. For this purpose, SWAN numerical model and ECMWF wind fields were used to produce the time series of wave characteristics over 25 years from 1984 till 2008. Moreover, three points in the western, central and eastern parts of the Persian Gulf were selected and the time series of energy extracted from the modeled waves were evaluated at these points. The results show that there are both seasonal and decadal variations in the wave energy trends in all considered points due to the climate variability. There was a reduction in wave power values from 1990 to 2000 in comparison with the previous and following years. Comparison of wind speed and *REVISED Manuscript UNMARKED Click here to view linked References corresponding wave power variations indicates that a small variation in the wind speed can cause a large variation in the wave power. The seasonal oscillations lead to variation of the wave power from the lowest value in summer to the highest value in winter in all considered stations. In addition, the seasonal trend of wave power changed during the decadal variation of wave power. Directional variations of wave power were also assessed during the decadal variations and the results showed that the dominant direction of wave propagation changed in the period of 1990 to 2000 especially in the western station.

Forecasting of wave parameters is necessary for many marine and coastal operations.Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, Artificial Neural Network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 hours in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous three hours, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 hours. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.

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