Pridiction of Sea Level Rise in the South of Iran Coastline: Evaluation of Climate Change Impacts
Subject Areas : Article frome a thesisHamid Goharnejad 1 , Abolfazl Shamsai 2 , sara نظیف 3 , Mahmood Zakeri Niri 4
1 - عضو هیات علمی دانشگاه آزاد اسلامی
2 - عضو هیات علمی
3 - استادیار دانشکده مهندسی عمران- پردیس دانشکده‌های فنی دانشگاه تهران – تهران –ایران
4 - Department of civil Engineering, islamshahr Branch, islamic Azad university, Islamshahr, Iran
Keywords: Climate change scenarios, Sea-level rise, DWNN, DWANFIS,
Abstract :
The investigations in recent two decades have demonstrated the global sea level rise which is much more related to climate change phenomena and its impacts. In this study the impact of climate change on sea level rise at the southern coastal line of Iran is evaluated. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b was 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 are selected for sea level rise prediction using stepwise regression. Two models of Discrete Wavelet Artificial Neural Network (DWNN) and Discrete Wavelet Adaptive Neuro-Fuzzy Inference system (DWANFIS) are developed to explore the relationship between climatic variables and sea level changes. In these models wavelet is used to disaggregate the time series of climatic variables as well as sea level data into different components and then ANFIS/ANN are used to relate the disaggregated components of predictors and predictands to each other. The results of this study show a significant increase of sea level in future under climate change impacts which should be incorporated in coastal areas management. The selected model (Anfis-Haar), which had a high performance index, showed that the sea level changes from 48 centimeters in the west of the Persian Gulf to 16 centimeters in the east of the Oman Sea. The changes in shallow and enclosed waters appear to be greater than other parts of studied area.
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