| Peer-Reviewed

Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model

Received: 7 October 2017     Accepted: 8 November 2017     Published: 11 December 2017
Views:       Downloads:
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.

Published in International Journal of Energy and Environmental Science (Volume 2, Issue 6)
DOI 10.11648/j.ijees.20170206.11
Page(s) 117-126
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Maximum Entropy Principle, 2D Compound Distribution Model, Extreme Wave Height, Extreme Water Level, Optimization, Climate Change

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.
Cite This Article
  • APA Style

    Baiyu Chen, Guilin Liu, Liping Wang. (2017). Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model. International Journal of Energy and Environmental Science, 2(6), 117-126. https://doi.org/10.11648/j.ijees.20170206.11

    Copy | Download

    ACS Style

    Baiyu Chen; Guilin Liu; Liping Wang. Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model. Int. J. Energy Environ. Sci. 2017, 2(6), 117-126. doi: 10.11648/j.ijees.20170206.11

    Copy | Download

    AMA Style

    Baiyu Chen, Guilin Liu, Liping Wang. Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model. Int J Energy Environ Sci. 2017;2(6):117-126. doi: 10.11648/j.ijees.20170206.11

    Copy | Download

  • @article{10.11648/j.ijees.20170206.11,
      author = {Baiyu Chen and Guilin Liu and Liping Wang},
      title = {Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model},
      journal = {International Journal of Energy and Environmental Science},
      volume = {2},
      number = {6},
      pages = {117-126},
      doi = {10.11648/j.ijees.20170206.11},
      url = {https://doi.org/10.11648/j.ijees.20170206.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijees.20170206.11},
      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.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Predicting Joint Return Period Under Ocean Extremes Based on a Maximum Entropy Compound Distribution Model
    AU  - Baiyu Chen
    AU  - Guilin Liu
    AU  - Liping Wang
    Y1  - 2017/12/11
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijees.20170206.11
    DO  - 10.11648/j.ijees.20170206.11
    T2  - International Journal of Energy and Environmental Science
    JF  - International Journal of Energy and Environmental Science
    JO  - International Journal of Energy and Environmental Science
    SP  - 117
    EP  - 126
    PB  - Science Publishing Group
    SN  - 2578-9546
    UR  - https://doi.org/10.11648/j.ijees.20170206.11
    AB  - 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.
    VL  - 2
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • College of Engineering, University of California Berkeley, Berkeley, USA

  • College of Engineering, Ocean University of China, Qingdao, China

  • School of Mathematical Sciences, Ocean University of China, Qingdao, China

  • Sections