Uncertainty analysis for the calculation of marine environmental design parameters in the South China Sea

Guilin LIU; Xinsheng ZHOU; Yi KOU; Fang WU; Daniel ZHAO; Yu XU

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Uncertainty analysis for the calculation of marine environmental design parameters in the South China Sea

Marine ecological environment and its response to marine dynamic processesin the South China Sea | Updated：2024-10-12

- Uncertainty analysis for the calculation of marine environmental design parameters in the South China Sea
- Journal of Oceanology and Limnology Vol. 41, Issue 2, Pages: 427-443(2023)
- Affiliations：
  
  1.College of Engineering, Ocean University of China, Qingdao 266071, China
  2.Dornsife College, University of Southern California, Los Angeles, CA 90089, USA
  3.Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA 93106, USA
  4.Department of Mathematics, Harvard University, Cambridge, MA 02138, USA
  5.Shandong Key Laboratory of Marine Engineering, Ocean University of China, Qingdao 266071, China
- Author bio：
  
  xuyu@ouc.edu.cn
- Funds：
- DOI：
  CLC：
- Received：30 January 2022，
  
  Published：01 March 2023
- Accepted：
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LIU Guilin,ZHOU Xinsheng,KOU Yi,et al.Uncertainty analysis for the calculation of marine environmental design parameters in the South China Sea[J].Journal of Oceanology and Limnology,2023,41(02):427-443.
DOI：

LIU Guilin,ZHOU Xinsheng,KOU Yi,et al.Uncertainty analysis for the calculation of marine environmental design parameters in the South China Sea[J].Journal of Oceanology and Limnology,2023,41(02):427-443. DOI：

摘要

Abstract

The calculation results of marine environmental design parameters obtained from different data sampling methods

model distributions

and parameter estimation methods often vary greatly. To better analyze the uncertainties in the calculation of marine environmental design parameters

a general model uncertainty assessment method is necessary. We proposed a new multivariate model uncertainty assessment method for the calculation of marine environmental design parameters. The method divides the overall model uncertainty into two categories: aleatory uncertainty and epistemic uncertainty. The aleatory uncertainty of the model is obtained by analyzing the influence of the number and the dispersion degree of samples on the information entropy of the model. The epistemic uncertainty of the model is calculated using the information entropy of the model itself and the prediction error. The advantages of this method are that it does not require many-year-observation data for the marine environmental elements

and the method can be used to analyze any specific factors that cause model uncertainty. Results show that by applying the method to the South China Sea

the aleatory uncertainty of the model increases with the number of samples and then stabilizes. A positive correlation was revealed between the dispersion of the samples and the aleatory uncertainty of the model. Both the distribution of the model and the parameter estimation results of the model have significant effects on the epistemic uncertainty of the model. When the goodness-of-fit of the model is relatively close

the best model can be selected according to the criterion of the lowest overall uncertainty of the models

which can both ensure a better model fit and avoid too much uncertainty in the model calculation results. The presented multivariate model uncertainty assessment method provides a criterion to measure the advantages and disadvantages of the marine environmental design parameter calculation model from the aspect of uncertainty

which is of great significance to analyze the uncertainties in the calculation of marine environmental design parameters and improve the accuracy of the calculation results.

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Keywords

references

Aarnes O J , Abdalla S , Bidlot J R et al . 2015 . Marine wind and wave height trends at different ERA-interim forecast ranges . Journal of Climate , 28 ( 2 ): 819 - 837 , https://doi.org/10.1175/JCLI-D-14-00470.1 https://doi.org/10.1175/JCLI-D-14-00470.1 .

Acitas S , Aladag C H , Senoglu B . 2019 . A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: an application to the strengths of glass fibre data . Reliability Engineering & System Safety , 183 : 116 - 127 , https://doi.org/10.1016/j.ress.2018.07.024 https://doi.org/10.1016/j.ress.2018.07.024 .

Akaike H . 2011 . Akaike's information criterion . In: Lovric M ed. International Encyclopedia of Statistical Science . Springer, Berlin, Heidelberg , https://doi.org/10.1007/978-3-642-04898-2 https://doi.org/10.1007/978-3-642-04898-2 .

Alexander C , Sarabia J M . 2012 . Quantile uncertainty and value-at-risk model risk . Risk Analysis , 32 ( 8 ): 1293 - 1308 , https://doi.org/10.1111/J.1539-6924.2012.01824.X https://doi.org/10.1111/J.1539-6924.2012.01824.X .

Bai X Y , Jiang H , Li C et al . 2020 . Joint probability distribution of coastal winds and waves using a log-transformed kernel density estimation and mixed copula approach . Ocean Engineering , 216 : 107937 , https://doi.org/10.1016/j.oceaneng.2020.107937 https://doi.org/10.1016/j.oceaneng.2020.107937 .

Blasone R S , Madsen H , Rosbjerg D . 2008 . Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling . Journal of Hydrology , 353 ( 1-2 ): 18 - 32 , https://doi.org/10.1016/J.JHYDROL.2007.12.026 https://doi.org/10.1016/J.JHYDROL.2007.12.026 .

Bruserud K , Haver S , Myrhaug D . 2018 . Joint description of waves and currents applied in a simplified load case . Marine Structures , 58 : 416 - 433 , https://doi.org/10.1016/j.marstruc.2017.12.010 https://doi.org/10.1016/j.marstruc.2017.12.010 .

Chen B Y , Kou Y , Wang Y F et al . 2021a . Analysis of storm surge characteristics based on stochastic process . IMS Mathematics , 6 ( 2 ): 1177 - 1190 , https://doi.org/10.3934/math.2021072 https://doi.org/10.3934/math.2021072 .

Chen B Y , Kou Y , Wu F et al . 2021b . Study on evaluation standard of uncertainty of design wave height calculation model . Journal of Oceanology and Limnology , 39 ( 4 ): 1188 - 1197 , https://doi.org/10.1007/S00343-020-0327-8 https://doi.org/10.1007/S00343-020-0327-8 .

Chen B Y , Kou Y , Zhao D L et al . 2021c . Maximum entropy distribution function and uncertainty evaluation criteria . China Ocean Engineering , 35 ( 2 ): 238 - 249 , https://doi.org/10.1007/S13344-021-0021-4 https://doi.org/10.1007/S13344-021-0021-4 .

Chen B Y , Zhang K Y , Wang L P et al . 2019a . Generalized extreme value-Pareto distribution function and its applications in ocean engineering . China Ocean Engineering , 33 ( 2 ): 127 - 136 , https://doi.org/10.1007/S13344-019-0013-9 https://doi.org/10.1007/S13344-019-0013-9 .

Chen W S , Mo J H , Du X et al . 2019b . Biomimetic dynamic membrane for aquatic dye removal . Water Research , 151 : 243 - 251 , https://doi.org/10.1016/j.watres.2018.11.078 https://doi.org/10.1016/j.watres.2018.11.078 .

Cont R . 2006 . Model uncertainty and its impact on the pricing of derivative instruments . Mathematical Finance , 16 ( 3 ): 519 - 547 , https://doi.org/10.1111/J.1467-9965.2006.00281.X https://doi.org/10.1111/J.1467-9965.2006.00281.X .

de Michele C , Salvadori G . 2005 . Some hydrological applications of small sample estimators of generalized Pareto and extreme value distributions . Journal of Hydrology , 301 ( 1-4 ): 37 - 53 , https://doi.org/10.1016/J.JHYDROL.2004.06.015 https://doi.org/10.1016/J.JHYDROL.2004.06.015 .

Silva V P R , Filho A F B , Almeida R S R et al . 2016 . Shannon information entropy for assessing space-time variability of rainfall and streamflow in semiarid region . Science of the Total Environment , 544 : 330 - 338 , https://doi.org/10.1016/j.scitotenv.2015.11.082 https://doi.org/10.1016/j.scitotenv.2015.11.082 .

Derwent R G . 2020 . Monte Carlo analyses of the uncertainties in the predictions from global tropospheric ozone models: tropospheric burdens and seasonal cycles . Atmospheric Environment , 231 : 117545 , https://doi.org/10.1016/j.atmosenv.2020.117545 https://doi.org/10.1016/j.atmosenv.2020.117545 .

Geman S , Geman D . 1984 . Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images . IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6 ( 6 ): 721 - 741 , https://doi.‍org/10.1109/TPAMI.1984.4767596 https://doi.‍org/10.1109/TPAMI.1984.4767596 . https://do 10.1109/tpami.1984.4767596 http://dx.doi.org/10.1109/tpami.1984.4767596

Grimaldi S , Serinaldi F . 2006 . Asymmetric copula in multivariate flood frequency analysis . Advances in Water Resources , 29 ( 8 ): 1155 - 1167 , https://doi.org/10.1016/j.advwatres.2005.09.005 https://doi.org/10.1016/j.advwatres.2005.09.005 .

Guachamin-Acero W , Li L . 2018 . Methodology for assessment of operational limits including uncertainties in wave spectral energy distribution for safe execution of marine operations . Ocean Engineering , 165 : 184 - 193 , https://doi.org/10.1016/j.oceaneng.2018.07.032 https://doi.org/10.1016/j.oceaneng.2018.07.032 .

Guan Q S , Peng W . 2015 . Parameter estimation for geometric-Gumbel compound extreme-value distribution based on the pi-th quantiles of samples . In: Proceedings of 2015 Conference on Informatization in Education, Management and Business . Atlantis Press, Guangzhou , p. 60 - 64 , https://doi.org/10.2991/iemb-15.2015.12 https://doi.org/10.2991/iemb-15.2015.12 .

Hora S C . 1996 . Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management . Reliability Engineering & System Safety , 54 ( 2-3 ): 217 - 223 , https://doi.org/10.1016/S0951-8320(96)00077-4 https://doi.org/10.1016/S0951-8320(96)00077-4 .

Huang W N , Dong S . 2020 . Joint distribution of individual wave heights and periods in mixed sea states using finite mixture models . Coastal Engineering , 161 : 103773 , https://doi.org/10.1016/j.coastaleng.2020.103773 https://doi.org/10.1016/j.coastaleng.2020.103773 .

Jiang D , Qian Y M . 1992 . Information Theory and Coding. University of Science and Technology of China, Hefei, China . p. 353 - 399 . (in Chinese)

Kurian V J , Nizamani Z , Liew M S . 2012 . Statistical modelling of environmental load uncertainty for jacket platforms in Malaysia . In: Proceedings of 2012 IEEE Colloquium on Humanities, Science and Engineering . IEEE, Kota Kinabalu, Malaysia . p. 74 - 79 , https://doi.org/10.1109/chuser.2012.6504284 https://doi.org/10.1109/chuser.2012.6504284 .

Lei F H , Xie B T , Wang J Q . 2012 . Uncertainty analysis of marine environment elements calculation . The Ocean Engineering , 30 ( 4 ): 109 - 117 , https://doi.org/10.16483/j.issn.1005-9865.2012.04.019. https://doi.org/10.16483/j.issn.1005-9865.2012.04.019. (in Chinese with English abstract)

Li Y X , Liu G L . 2020 . Risk analysis of marine environmental elements based on Kendall return period . Journal of Marine Science and Engineering , 8 ( 6 ): 393 , https://doi.org/10.3390/JMSE8060393 https://doi.org/10.3390/JMSE8060393 .

Liu D F , Dong S , Wang C . 1996 . Uncertainty and sensitivity analysis of reliability for marine structures . In: Proceedings of the 6th International Offshore and Polar Engineering Conference . ISOPE, Los Angeles, USA . p. 380 - 386 .

Liu D F , Wang L P , Pang L . 2006 . Theory of multivariate compound extreme value distribution and its application to extreme sea state prediction . Chinese Science Bulletin , 51 ( 23 ): 2926 - 2930 , https://doi.org/10.1007/S11434-006-2186-X https://doi.org/10.1007/S11434-006-2186-X .

Liu G L , Chen B Y , Gao Z K et al . 2019a . Calculation of joint return period for connected edge data . Water , 11 ( 2 ): 300 , https://doi.org/10.3390/w11020300 https://doi.org/10.3390/w11020300 .

Liu G L , Chen B Y , Jiang S et al . 2019b . Double entropy joint distribution function and its application in calculation of design wave height . Entropy , 21 ( 1 ): 64 , https://doi.org/10.3390/E21010064 https://doi.org/10.3390/E21010064 .

Liu G L , Cui K , Jiang S et al . 2021 . A new empirical distribution for the design wave heights under the impact of typhoons . Applied Ocean Research , 111 : 102679 , https://doi.org/10.1016/j.apor.2021.102679 https://doi.org/10.1016/j.apor.2021.102679 .

Liu G L , Yu Y H , Kou Y et al . 2020 . Joint probability analysis of marine environmental elements . Ocean Engineering , 215 : 107879 , https://doi.org/10.1016/j.oceaneng.2020.107879 https://doi.org/10.1016/j.oceaneng.2020.107879 .

Liu J S . 1996 . Peskun's theorem and a modified discrete-state Gibbs sampler . Biometrika , 83 ( 3 ): 681 - 682 , https://doi.org/10.1093/biomet/83.3.681 https://doi.org/10.1093/biomet/83.3.681 .

Ma C H , Huang Q , Guo A J . 2019 . Characteristic analysis and uncertainty assessment of joint distribution of flow and sand in Jinghe River basin . Journal of Hydraulic Engineering , 50 ( 2 ): 273 - 282 , https://doi.org/10.13243/j.cnki.slxb.20180669. https://doi.org/10.13243/j.cnki.slxb.20180669. (in Chinese with English abstract)

Ma P F , Zhang Y . 2022 . Modeling asymmetrically dependent multivariate ocean data using truncated copulas . Ocean Engineering , 244 : 110226 , https://doi.org/10.1016/j.oceaneng. 2021.110226 https://doi.org/10.1016/j.oceaneng.2021.110226 .

Meng S J , Meng X H , Fan W H et al . 2020 . The role of transparent exopolymer particles (TEP) in membrane fouling: a critical review . Water Research , 181 : 115930 , https://doi.org/10.1016/j.watres.2020.115930 https://doi.org/10.1016/j.watres.2020.115930 .

Negnevitsky M , Terry J , Nguyen T . 2014 . Using information entropy to quantify uncertainty in distribution networks . In: Proceedings of 2014 Australasian Universities Power Engineering Conference . IEEE, Perth, Australia . p. 1 - 6 , https://doi.org/10.1109/AUPEC.2014.6966487 https://doi.org/10.1109/AUPEC.2014.6966487 .

Nugues P M . 2014 . Topics in information theory and machine learning . In: Nugues P M ed. Language Processing with Perl and Prolog . Springer, Berlin, Heidelberg . p.‍ 87 - 121 , https://doi.org/10.1007/978-3-642-41464-0_4 https://doi.org/10.1007/978-3-642-41464-0_4 .

Panchang V , Zhao L Z , Demirbilek Z . 1998 . Estimation of extreme wave heights using GEOSAT measurements . Ocean Engineering , 26 ( 3 ): 205 - 225 , https://doi.org/10.1016/S0029-8018(97)10026-9 https://doi.org/10.1016/S0029-8018(97)10026-9 .

Petrov V , Guedes Soares C , Gotovac H . 2013 . Prediction of extreme significant wave heights using maximum entropy . Coastal Engineering , 74 : 1 - 10 , https://doi.org/10.1016/j.coastaleng.2012.11.009 https://doi.org/10.1016/j.coastaleng.2012.11.009 .

Shannon C E . 1948 . A mathematical theory of communication . The Bell System Technical Journal , 27 ( 3 ): 379 - 423 , https://doi.org/10.1002/j.1538-7305.1948.tb01338.x https://doi.org/10.1002/j.1538-7305.1948.tb01338.x .

Silva-González F , Heredia-Zavoni E , Inda-Sarmiento G . 2017 . Square error method for threshold estimation in extreme value analysis of wave heights . Ocean Engineering , 137 : 138 - 150 , https://doi.org/10.1016/j.oceaneng.2017.03.028 https://doi.org/10.1016/j.oceaneng.2017.03.028 .

Swendsen R H , Wang J S . 1987 . Nonuniversal critical dynamics in Monte Carlo simulations . Physical Review Letters , 58 ( 2 ): 86 - 88 , https://doi.org/10.1103/physrevlett.58.86 https://doi.org/10.1103/physrevlett.58.86 .

Tapiero O J . 2013 . The relationship between risk and incomplete states uncertainty: a Tsallis entropy perspective . Algorithmic Finance , 2 ( 2 ): 141 - 150 , https://doi.org/10.3233/AF-13022 https://doi.org/10.3233/AF-13022 .

Vanem E . 2016 . Joint statistical models for significant wave height and wave period in a changing climate . Marine Structures , 49 : 180 - 205 , https://doi.org/10.1016/j.marstruc.2016.06.001 https://doi.org/10.1016/j.marstruc.2016.06.001 .

Wang L P , Chen B Y , Chen C et al . 2016 . Application of linear mean-square estimation in ocean engineering . China Ocean Engineering , 30 ( 1 ): 149 - 160 , https://doi.org/10.1007/S13344-016-0007-9 https://doi.org/10.1007/S13344-016-0007-9 .

Wist H T , Myrhaug D , Rue H . 2004 . Statistical properties of successive wave heights and successive wave periods . Applied Ocean Research , 26 ( 3-4 ): 114 - 136 , https://doi.org/10.1016/j.apor.2005.01.002 https://doi.org/10.1016/j.apor.2005.01.002 .

Wu M N , Gao Z . 2021 . Methodology for developing a response-based correction factor (alpha-factor) for allowable sea state assessment of marine operations considering weather forecast uncertainty . Marine Structures , 79 : 103050 , https://doi.org/10.1016/J.MARSTRUC.2021.103050 https://doi.org/10.1016/J.MARSTRUC.2021.103050 .

Wu M N , Stefanakos C , Gao Z et al . 2019 . Prediction of short-term wind and wave conditions for marine operations using a multi-step-ahead decomposition-ANFIS model and quantification of its uncertainty . Ocean Engineering , 188 : 106300 , https://doi.org/10.1016/j.oceaneng.2019.106300 https://doi.org/10.1016/j.oceaneng.2019.106300 .

Xu S , Guedes Soares C . 2021 . Evaluation of spectral methods for long term fatigue damage analysis of synthetic fibre mooring ropes based on experimental data . Ocean Engineering , 226 : 108842 , https://doi.org/10.1016/J.OCEANENG.2021.108842 https://doi.org/10.1016/J.OCEANENG.2021.108842 .

Zachary S , Feld G , Ward G et al . 1998 . Multivariate extrapolation in the offshore environment . Applied Ocean Research , 20 ( 5 ): 273 - 295 , https://doi.org/10.1016/S0141-1187(98)00027-3 https://doi.org/10.1016/S0141-1187(98)00027-3 .

Zhai J J , Yin Q L , Dong S . 2017 . Metocean design parameter estimation for fixed platform based on copula functions . Journal of Ocean University of China , 16 ( 4 ): 635 - 648 , https://doi.org/10.1007/S11802-017-3327-3 https://doi.org/10.1007/S11802-017-3327-3 .

Zhang H D , Guedes Soares C . 2016 . Modified joint distribution of wave heights and periods . China Ocean Engineering , 30 ( 3 ): 359 - 374 , https://doi.org/10.1007/S13344-016-0024-8 https://doi.org/10.1007/S13344-016-0024-8 .

Zhang H D , Liao X M , Shi H D et al . 2022 . Effect of initial condition uncertainty on the profile of maximum wave . Marine Structures , 82 : 103127 , https://doi.org/10.1016/j.marstruc.2021.103127 https://doi.org/10.1016/j.marstruc.2021.103127 .

Zhang S , Solari G , Yang Q S et al . 2018 . Extreme wind speed distribution in a mixed wind climate . Journal of Wind Engineering and Industrial Aerodynamics , 176 : 239 - 253 , https://doi.org/10.1016/J.JWEIA.2018.03.019 https://doi.org/10.1016/J.JWEIA.2018.03.019 .

Zhang W X , Grimi N , Jaffrin M Y et al . 2015a . Leaf protein concentration of alfalfa juice by membrane technology . Journal of Membrane Science , 489 : 183 - 193 , https://doi.org/10.1016/j.memsci.2015.03.092 https://doi.org/10.1016/j.memsci.2015.03.092 .

Zhang W X , Jiang F . 2019 . Membrane fouling in aerobic granular sludge (AGS)-membrane bioreactor (MBR): effect of AGS size . Water Research , 157 : 445 - 453 , https://doi.org/10.1016/j.watres.2018.07.069 https://doi.org/10.1016/j.watres.2018.07.069 .

Zhang W X , Liang W Z , Zhang Z E et al . 2021 . Aerobic Granular Sludge (AGS) scouring to mitigate membrane fouling: performance, hydrodynamic mechanism and contribution quantification model . Water Research , 188 : 116518 , https://doi.org/10.1016/j.watres.2020.116518 https://doi.org/10.1016/j.watres.2020.116518 .

Zhang Y . 2015 . On the climatic uncertainty to the environment extremes: a Singapore case and statistical approach . Polish Journal of Environmental Studies , 24 ( 3 ): 1413 - 1422 , https://doi.org/10.15244/pjoes/31718 https://doi.org/10.15244/pjoes/31718 .

Zhang Y , Cao Y Y . 2015 . A fuzzy quantification approach of uncertainties in an extreme wave height modeling . Acta Oceanologica Sinica , 34 ( 3 ): 90 - 98 , https://doi.org/10.1007/s13131-015-0636-5 https://doi.org/10.1007/s13131-015-0636-5 .

Zhang Y , Cao Y Y , Dai J . 2015b . Quantification of statistical uncertainties in performing the peak over threshold method . Journal of Marine Science and Technology , 23 ( 5 ): 15 , https://doi.org/10.6119/JMST-015-0604-1 https://doi.org/10.6119/JMST-015-0604-1 .

Zhang Y , Lee Lam J S . 2014 . Non-conventional modeling of extreme significant wave height through random sets . Acta Oceanologica Sinica , 33 ( 7 ): 125 - 130 , https://doi.org/10.1007/s13131-014-0508-4 https://doi.org/10.1007/s13131-014-0508-4 .

Zhu Z Z , Chen Z , Luo X et al . 2020 . Gravity-Driven Biomimetic Membrane (GDBM): an ecological water treatment technology for water purification in the open natural water system . Chemical Engineering Journal , 399 : 125650 , https://doi.org/10.1016/j.cej.2020.125650 https://doi.org/10.1016/j.cej.2020.125650 .

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