Interaction between wind waves and swell in the Persian Gulf

The effect of swell on wind wave growth has been a topic of active research for many years with inconsistent results. The details are often contradictory among investigations. Further more, there remain a variety of competing theories to explain these phenomena. In this research, we consider waves and wind and temperature data in the Persian Gulf (Busher region) in years 1995, 1996 and 1999. This study provides estimations of wave conditions and the atmosphere stability that has an influence on wind wave. Results are also compared with data that have been recorded by a buoy in Caspian Sea (Neka region) during 1989. In the second part of this work we estimate non- dimensional energy and non-dimensional peak frequencies as a function of the non- dimensional fetch and Bulk Richardson numbers for the Persian Gulf (Busher region).This results also agree well with similar results for the Caspian Sea. The acquired relations can be used to compute the wind wave parameters. Also the results for the Persian Gulf show that the relationship of non-dimensional energy to as a function of wave age is independent of presence of swell. Finally the WAM model was run for the Persian Gulf during 3-8 September of 2002. The results show that swell on the Persian Gulf reduces the energy density of wind waves by up to 10%, but the growth rate at peak frequency is only reduced by up to 4%, and the spectral peak frequency is increased by only 1%.

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.