Wind wave prediction in the Caspian Sea

In this thesis, wind wave prediction and analysis in the Southern Caspian Sea are surveyed. Because of very much importance and application of this matter in reducing vital and financial damages or marine activities, such as monitoring marine pollution, designing marine structure, shipping, fishing, offshore industry, tourism and etc, gave attention by some marine activities. In this study are used the Caspian Sea topography data that are extracted from the Caspian Sea Hydrography map of Iran Armed Forces Geographical Organization and the I 0 meter wind field data that are extracted from the transmitted GTS synoptic data of regional centers to Forecasting Center of Iran Meteorological Organization for wave prediction and is used the 20012 wave are recorded by the oil company's buoy that was located at distance 28 Kilometers from Neka shore for wave analysis. The results of this research are as follows: - Because of disagreement between the prediction results of SMB method in the Caspian sea and wave data of the Anzali and Neka buoys. The SMB method isn't able to Predict wave characteristics in the Southern Caspian Sea. - Because of good relativity agreement between the WAM model output in the Caspian Sea and wave data of the Anzali buoy. The WAM model is able to predict wave characteristics in the southern Caspian Sea with high relativity accuracy. The extreme wave height distribution function for fitting to the Southern Caspian Sea wave data is obtained by determining free parameters of Poisson-Gumbel function through moment method. These parameters are as below: A=2.41, B=0.33. The maximum relative error between the estimated 4-year return value of the Southern Caspian Sea significant wave height by above function with the wave data of Neka buoy is about %35. The 100-year return value of the Southern Caspian Sea significant height wave is about 4.97 meter. The maximum relative error between the estimated 4-year return value of the Southern Caspian Sea significant wave height by statistical model of peak over threshold with the wave data of Neka buoy is about %2.28. The parametric relation for fitting to the Southern Caspian Sea frequency spectra is obtained by determining free parameters of the Strekalov, Massel and Krylov etal_ multipeak spectra through mathematical method. These parameters are as below: A = 2.9 B=26.26, C=0.0016 m=0.19 and n=3.69. The maximum relative error between calculated free parameters of the Southern Caspian Sea multipeak spectrum with the proposed free parameters of double-peaked spectrum by Massel and Strekalov on the experimental data from the Caspian Sea is about 36.1 % in spectrum energetic part and is about 74M% in spectrum high frequency part. The peak over threshold waverose of the Southern Caspian Sea shows that maximum occurrence probability of wave height is relevant to waves with 2-2.5 meters wave fhe error sources in the statistical analysis are mainly due to: l) the missing wave data in 2 years duration through battery discharge of Neka buoy. 2) the deportation %15 of significant height annual mean in single year than long period average value that is caused by lack of adequate measurement on oceanic waves, and the error sources in the spectral analysis are mainly due to above- mentioned items and low accurate of the proposed free parameters of double-peaked spectrum on the experimental data from the Caspian Sea.

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.