Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model
International Journal of Energy and Environmental Science
Volume 2, Issue 6, November 2017, Pages: 117-126
Received: Oct. 7, 2017; Accepted: Nov. 8, 2017; Published: Dec. 11, 2017
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Baiyu Chen, College of Engineering, University of California Berkeley, Berkeley, USA
Guilin Liu, College of Engineering, Ocean University of China, Qingdao, China
Liping Wang, School of Mathematical Sciences, Ocean University of China, Qingdao, China
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In this paper, we proposed a novel 2-dimensional (2D) distribution model based on the maximum-entropy (ME) principle to predict the joint return period under ocean extremes. In detail, we first derive the joint probability distribution of the extreme wave heights and the extreme water-levels during a typhoon by using the maximum-entropy principle, and then we nest this distribution with the maximum-entropy distribution of discrete variables to form such a maximum-entropy 2-dimensional (ME 2D) compound distribution model. To evaluate the performance of our model, we conduct experiments to predict the N-year joint return-periods of the extreme wave heights and the extreme water levels in two areas of the East China Sea. According to the experimental results, our model performs better in predicting in the highly unpredictable joint probability of extreme wave heights and water levels in typhoon affected sea areas, compared with the widely-used Poisson-Mixed-Gumbel model in ocean engineering design. This ascribes to the fact that unlike other models whose corresponding parameters are arbitrarily assigned, our model utilizes both the new 2D distribution and the discrete distribution which are based on the ME principle.
Maximum Entropy Principle, 2D Compound Distribution Model, Extreme Wave Height, Extreme Water Level, Optimization, Climate Change
To cite this article
Baiyu Chen, Guilin Liu, Liping Wang, Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model, International Journal of Energy and Environmental Science. Vol. 2, No. 6, 2017, pp. 117-126. doi: 10.11648/j.ijees.20170206.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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