Abstract—Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study, climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One of models (Discrete Wavelet artificial Neural Network) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and input parameters to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 t0 105 cm. Furthermore, the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management. Keywords—Climate change scenarios, sea-level rise, strait of Hormuz, artificial neural network, fuzzy logic. I. INTRODUCTION TUDIES show, Increasing water level of seas and oceans due to warming of the Earth's atmosphere over the past century, massive melting of ice and water is expanding [4]. If sea level rise occurs, it would be a significant impact on the coastline. The sea level rise in people's lives and the economy of coastal areas, especially in areas adjacent to the sea, which always have high flood potential, will have a significant impact [7]. The recent investigations all over the world demonstrated the global temperature (land and sea) increase by 0.76°C from 1850–1899 to 2001–2005 [3]. This temperature increase is more intensify in the northern hemisphere. In some years (e.g., 1998 and 2005) it the temperature has increased more than 1°C [3]. The global warming highly affects the living environment. These effects may result in changes in river flow patterns and sea level. Based on the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC), for the 20th Century, the average rate of global mean sea level rise (SLR) was estimated about 0.5- 1.7 mm/yr [1]. It was predicted that the global sea level rise by Hamid Goharnejad and Amir Hossein Eghbali are with the Department of Civil Engineering, Eslamshahr Branch, Islamic Azad, University (IAU), Eslamshahr, Iran (e-mail: h.goharnejad@iiau.ac.ir, eghbali@iiau.ac.ir). the end of the 21st Century would be between 0.18 m and 0.4 m [1]. The results of analyses on tide gauge records and satellite altimetry in open oceans showed that the range of sea level changes is different in various regions [1]. In some cases, the predicted SLR is higher than the global mean whilst in some cases predictions show sea level descends. The assessment of coastal zone vulnerability to sea level rise and application of adaptation strategies are the key issues in dealing with climate change impacts. The projections of global sea level changes under probable climate change scenarios showed that the flooding risk is increasing over low lying coastal regions [3]. The recent applications of Artificial Intelligence (AI) techniques have demonstrated their high capability in dealing with stochastic time series such as sea level. When the underlying physical relationships in time series are not fully understood, AI techniques provide an effective approach to model them. Two of these models which are widely used in recent decades especially in field of hydrological analyses and for prediction purposes, are artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) models [7]. ANNs have been used widely and successfully for hydrological modeling as well as prediction purposes because of their ability to discover patterns in data that cannot be explored by human researchers and conventional statistical methods [1]. The use of ANFIS model which is a hybrid of ANN and fuzzy system has been a research focus to provide the opportunity to use the advantages of both ANN and fuzzy systems [3]. In spite of high flexibility of ANN and ANFIS, in case of sea level rise prediction the results of their application are not that much satisfactory because signal fluctuations are highly non-stationary and the physical hydrologic process operates under a large range of scales. In these cases pre- processing of the input and/or output data before feeding into prediction model, is necessary [6]. Assess the vulnerability of coastal areas due to sea level rise and the use of appropriate strategies, key issues facing the effects of climate change. Forecasts of global sea level change under different scenarios of possible climate change, show flood risk in coastal areas because of rising sea levels and increased depending on the increase in water level, the vulnerability of coasts and ports also will vary [7]. The objective of this study was to investigate the effects of climate change on sea level changes in the Strait of Hormuz and the vulnerability of the region's water level is rising. Hamid Goharnejad, Amir Hossein Eghbali Forecasting the Sea Level Change in Strait of Hormuz S World Academy of Science, Engineering and Technology International Journal of Geological and Environmental Engineering Vol:9, No:11, 2015 1329 International Scholarly and Scientific Research & Innovation 9(11) 2015 scholar.waset.org/1307-6892/10002861 International Science Index, Geological and Environmental Engineering Vol:9, No:11, 2015 waset.org/Publication/10002861