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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Authors
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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Abstract
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.
Keywords
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
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
K. Emanuel, “Increasing destructiveness of tropical cyclones over the past 30 year”, Nature, 2005, 436 (7051): 686-688.
[2]
L. Wang, et al., “A new model for calculating the design wave height in typhoon-affected sea areas”, Nat Hazar 67 (2): 129-143, 2013.
[3]
L. Wang, et al., “A New Method to Estimate Wave Height of Specified Return Period”, Chinese Journal of Oceanology and Limnology. 2017, 35 (5).
[4]
Y. Cao, et al., “A VEDA simulation on cement paste: using dynamic atomic force microscopy to characterize cellulose nanocrystal distribution”, MRS Communications, 7, 2017.
[5]
S. Wei, et al., “Analysis of wave motion in one-dimensional structures through fast-Fourier-transform-based wavelet finite element method”, Journal of Sound and Vibration 400 (2017): 369-386.
[6]
Y. Cao, et al., “The Influence of Cellulose Nanocrystal Additions on the Performance of Cement Paste”, Cement and Concrete Composites, 56, p73-83, 2015.
[7]
J. Robert, et al., “The influence of cellulose nanocrystals on the microstructure of cement paste”, Cement and Concrete Composites, 74, p164-173, 2016.
[8]
Y. Cao, et al., “The relationship between cellulose nanocrystal dispersion and strength”, Construction and Building Materials, 119, p71–79, 2016.
[9]
Z. Zhe, et al., “A thermography-based method for fatigue behavior evaluation of coupling beam damper”, Fracture and Structural Integrity 40 (2017): 149-161.
[10]
L. Li, et al., “Corrosion Monitoring and Evaluation of Reinforced Concrete Structures Utilizing the Ultrasonic Guided Wave Technique”, [J] Distributed Sensor Networks 10, no. 2 (2014).
[11]
J. Xu, et al., “Magnetic Transforms of Modulus Type Applied in Regions of Low Latitudes in SE China”, Journal of Applied Geophysics. 2017, 139: 188~194.
[12]
Z. Zhe, et al., “Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures”, Applied Sciences 7, no. 2 (2017): 137.
[13]
L. Wang, et al., “Application of linear mean-square estimation in ocean engineering”,China Ocean Engineering, 30 (1) 149-160, 2016.
[14]
B. Chen, et al., “Overcoming calibration problems in pattern labeling with pairwise ratings: application to personality traits”, Computer Vision–ECCV 2016 Workshops, 419-432.
[15]
T. Ulrych, “Maximum Entropy Spectral Analysis and Autoregressive Decomposition”, Rev Geophysics Space Phys, 1975, 13 (1): 183-200.
[16]
J. Xu, et al., “GPR Data Reconstruction Method Based on Compressive Sensing and K-SVD”, [J]. Near Surface Geophysics. 2017, 15 (4): 517~524.
[17]
V. Ponce-López, et al., “ChaLearn LAP 2016: First Round Challenge on First Impressions-Dataset and Results”, Computer Vision–ECCV 2016 Workshops, 400-418.
[18]
W. Feller, “An Introduction to Probability Theory and Its Applications (2nd ed)”, [M]. New York: John Willey, 1957.
[19]
J. Xu, et al., “Sensitivity Analysis of the Influence Factors of Slope Stability Based on LS-SVM”, [J]. Geomechanics and Engineering. 2017, 13 (3): 447~458.
[20]
L. Li, et al., “Pure density functional for strong correlation and the thermodynamic limit from machine learning”, Physical Review B 94.24 (2016): 245129.
[21]
J. Xu, et al., “Test and Analysis of Hydraulic Fracture Characteristics of Rock Single Crack”, [J]. Fluid Mechanics: Open Access. 2017, 4 (3): 164~167.
[22]
J. Xu, et al., “Low Strain Testing of Pile Based on Synchrosqueezing Wavelet Transformation Analysis”, [J]. Journal of Vibroengineering. 2016, 18 (2): 813-825.
[23]
Q. Ren, et al., “Prediction of the Strength of Concrete Radiation Shielding based on LS-SVM”, [J]. Annals of Nuclear Energy. 2015, 85 (0): 296-300.
[24]
L. Li, et al. “Understanding machine‐learned density functionals”, International Journal of Quantum Chemistry 116.11 (2016): 819-833.
[25]
J. Xu, et al., “Simulation Analysis of Low Strain Dynamic Testing of Pile with Inhomogeneous Elastic Modulus”, [J]. Journal of Measurements in Engineering. 2017, 5 (3): 152~160.
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