Forecasting the Sea Level Change in Strait of Hormuz
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.
[1] H. Goharnejad, A. Shamsai, S. A. Hosseini, “Vulnerability assessment
of southern coastal areas of Iran to sea level rise: evaluation of climate
change impact,” OCEANOLOGIA, 55 (3), 2013. pp. 611–637.
[2] Intergovernmental Panel on Climate Change, 2000. Climate Change:
The IPCC Scientific Assessment. Cambridge University Press, New
York, NY.
[3] IPCC. Climate Change 2007: The Physical Science Basis (eds Solomon,
S. et al.) (Cambridge Univ. Press, Cambridge, UK, and New York,
2007).
[4] Labat, D., 2005. Recent advances in wavelet analyses: Part 1. A review
of concepts. Journal of Hydrology 314 (1–4), 275–288.
[5] Lu, R. Y., 2002. Decomposition of interdecadal and interannual
components for North China rainfall in rainy season. Chinese Journal of
Atmosphere 26, 611–624.
[6] Makarynskyy. O., Makarynska. D., Kuhn. M., Featherstone. W.E., 2004,
Predicting sea level variations with artificial neural networks at Hillarys
Boat Harbour, Western Australia, Estuarine, Coastal and Shelf Science.
351–360.
[7] Mallat, S., 1998. A Wavelet Tour of Signal Processing. Academic Press.
Elsevier, UK.
[1] H. Goharnejad, A. Shamsai, S. A. Hosseini, “Vulnerability assessment
of southern coastal areas of Iran to sea level rise: evaluation of climate
change impact,” OCEANOLOGIA, 55 (3), 2013. pp. 611–637.
[2] Intergovernmental Panel on Climate Change, 2000. Climate Change:
The IPCC Scientific Assessment. Cambridge University Press, New
York, NY.
[3] IPCC. Climate Change 2007: The Physical Science Basis (eds Solomon,
S. et al.) (Cambridge Univ. Press, Cambridge, UK, and New York,
2007).
[4] Labat, D., 2005. Recent advances in wavelet analyses: Part 1. A review
of concepts. Journal of Hydrology 314 (1–4), 275–288.
[5] Lu, R. Y., 2002. Decomposition of interdecadal and interannual
components for North China rainfall in rainy season. Chinese Journal of
Atmosphere 26, 611–624.
[6] Makarynskyy. O., Makarynska. D., Kuhn. M., Featherstone. W.E., 2004,
Predicting sea level variations with artificial neural networks at Hillarys
Boat Harbour, Western Australia, Estuarine, Coastal and Shelf Science.
351–360.
[7] Mallat, S., 1998. A Wavelet Tour of Signal Processing. Academic Press.
Elsevier, UK.
@article{"International Journal of Earth, Energy and Environmental Sciences:71350", author = "Hamid Goharnejad and Amir Hossein Eghbali", title = "Forecasting the Sea Level Change in Strait of Hormuz", 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.", volume = "9", number = "11", pages = "1321-4", }