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Investigating the Role of Atmospheric Variables on Sea Level Variations in the Eastern Central Red Sea Using an Artificial Neural Network Approach
Oceanologia ( IF 2.6 ) Pub Date : 2020-02-24 , DOI: 10.1016/j.oceano.2020.02.002
Khalid M. Zubier , Lina S. Eyouni

Atmospheric variables play a major role in sea level variations in the eastern central Red Sea, where the role of tides is limited to 20% or less. Extensive analysis of daily-averaged residual sea level and atmospheric variables (atmospheric pressure, air temperature, wind stress components, and evaporation rate) indicated that sea level variations in the eastern central Red Sea are mainly contributed to by the seasonal and weather-band variations in the utilized atmospheric variables. The Non-linear Auto-Regressive Network with eXogenous inputs (NARX), a type of Artificial Neural Network (ANN), was applied to investigate the role of the atmospheric variables on the sea level variations at the eastern central Red Sea. Forced by time-delayed daily-averaged observations of atmospheric variables and residual sea level, the constructed NARX-based model showed high performance in predicting the one-step-ahead residual sea level. The high performance indicated that the constructed model was able to efficiently recognize the role played by the atmospheric variables on the residual sea level variations. Further investigations, using the constructed NARX-based model, revealed the seasonal variation in the role of the atmospheric variables. The study also revealed that the role played by some of the atmospheric variables, on sea level variations, could be masked by the role of one or more of the other atmospheric variables. The obtained results clearly demonstrated that this neurocomputing (NARX) approach is effective in investigating the individual and combined role of the atmospheric variables on residual sea level variations.



中文翻译:

使用人工神经网络方法研究东部中部红海大气变量对海平面变化的作用

大气变量在红海东部中部海平面变化中起主要作用,那里的潮汐作用被限制在20%或以下。对每日平均剩余海平面和大气变量(大气压力,气温,风应力分量和蒸发速率)的大量分析表明,红海东部中部的海平面变化主要是由季节和天气带变化造成的在利用的大气变量中。带有人工输入的非线性自回归网络(NARX)是一种人工神经网络(ANN),用于研究大气变量对红海东部中部海平面变化的影响。受到对大气变量和残留海平面的时延每日平均观测值的强迫,构建的基于NARX的模型在预测一步一步剩余海平面方面表现出了很高的性能。高性能表明构建的模型能够有效识别大气变量对残余海平面变化的作用。使用构建的基于NARX的模型进行的进一步研究揭示了大气变量作用的季节性变化。该研究还表明,某些大气变量在海平面变化上所起的作用可能被一个或多个其他大气变量的作用所掩盖。获得的结果清楚地表明,这种神经计算(NARX)方法可有效地研究大气变量对残余海平面变化的个体作用和综合作用。

更新日期:2020-02-24
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