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

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.

[1]  Jinping Ou,et al.  A thermography-based method for fatigue behavior evaluation of coupling beam damper , 2017 .

[3]  T. Ulrich,et al.  Maximum entropy spectral analy-sis and autoregressive decomposition , 1975 .

[4]  Sergio Escalera,et al.  Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits , 2016, ECCV Workshops.

[5]  Wei Yang,et al.  Corrosion Monitoring and Evaluation of Reinforced Concrete Structures Utilizing the Ultrasonic Guided Wave Technique , 2014, Int. J. Distributed Sens. Networks.

[7]  Robert J. Moon,et al.  The influence of cellulose nanocrystal additions on the performance of cement paste , 2015 .

[8]  Shen Zhenzhong,et al.  Prediction of the strength of concrete radiation shielding based on LS-SVM , 2015 .

[9]  Jinping Ou,et al.  Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures , 2017 .

[10]  Pablo D. Zavattieri,et al.  The influence of cellulose nanocrystals on the microstructure of cement paste , 2016 .

[11]  P. Zavattieri,et al.  The relationship between cellulose nanocrystal dispersion and strength , 2016 .

[12]  Zheng-Shou Chen,et al.  A new method to estimate wave height of specified return period , 2017, Chinese Journal of Oceanology and Limnology.

[13]  Li Li,et al.  Understanding Machine-learned Density Functionals , 2014, ArXiv.

[14]  Zhenzhong Shen,et al.  Test and Analysis of Hydraulic Fracture Characteristics of Rock Single Crack , 2017 .

[15]  Jinping Ou,et al.  Analysis of wave motion in one-dimensional structures through fast-Fourier-transform-based wavelet finite element method , 2017 .

[16]  K. Emanuel.  Increasing destructiveness of tropical cyclones over the past 30 years , 2005, Nature.

[17]  Sergio Escalera,et al.  ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results , 2016, ECCV Workshops.

[18]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[19]  Chao Chen,et al.  Application of linear mean-square estimation in ocean engineering , 2016 .

[20]  Zhenzhong Shen,et al.  GPR data reconstruction method based on compressive sensing and K-SVD , 2018 .

[21]  Zheng-Shou Chen,et al.  A new model for calculating the design wave height in typhoon-affected sea areas , 2013, Natural Hazards.

[22]  Kieron Burke,et al.  Pure density functional for strong correlation and the thermodynamic limit from machine learning , 2016, 1609.03705.