Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
20-01734S
Grantová Agentura České Republiky
PubMed
34205304
PubMed Central
PMC8234154
DOI
10.3390/e23060778
PII: e23060778
Knihovny.cz E-zdroje
- Klíčová slova
- entropy measures, importance measure, sensitivity analysis, uncertainty quantification,
- Publikační typ
- časopisecké články MeSH
Differential entropy can be negative, while discrete entropy is always non-negative. This article shows that negative entropy is a significant flaw when entropy is used as a sensitivity measure in global sensitivity analysis. Global sensitivity analysis based on differential entropy cannot have negative entropy, just as Sobol sensitivity analysis does not have negative variance. Entropy is similar to variance but does not have the same properties. An alternative sensitivity measure based on the approximation of the differential entropy using dome-shaped functionals with non-negative values is proposed in the article. Case studies have shown that new sensitivity measures lead to a rational structure of sensitivity indices with a significantly lower proportion of higher-order sensitivity indices compared to other types of distributional sensitivity analysis. In terms of the concept of sensitivity analysis, a decrease in variance to zero means a transition from the differential to discrete entropy. The form of this transition is an open question, which can be studied using other scientific disciplines. The search for new functionals for distributional sensitivity analysis is not closed, and other suitable sensitivity measures may be found.
Zobrazit více v PubMed
Sobol I.M. Sensitivity estimates for non-linear mathematical models. Math. Model. Comput. Exp. 1993;1:407–414.
Sobol I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 2001;55:271–280. doi: 10.1016/S0378-4754(00)00270-6. DOI
Amigó J.M., Balogh S.G., Hernández S. A brief review of generalized entropies. Entropy. 2018;20:813. doi: 10.3390/e20110813. PubMed DOI PMC
Castaings W., Borgonovo E., Morris M.D., Tarantola S. Sampling strategies in density-based sensitivity analysis. Environ. Model Softw. 2012;38:13–26. doi: 10.1016/j.envsoft.2012.04.017. DOI
Pianosi F., Wagener T. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ. Modell. Softw. 2015;67:1–11. doi: 10.1016/j.envsoft.2015.01.004. DOI
Borgonovo E., Plischke E. Sensitivity analysis: A review of recent advances. Eur. J. Oper. Res. 2016;248:869–887. doi: 10.1016/j.ejor.2015.06.032. DOI
Borgonovo E., Plischke E., Rakovec O., Hill M.C. Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res. 2017;53:7933–7950. doi: 10.1002/2017WR020767. DOI
Pianosi F., Wagener T. Distribution-based sensitivity analysis from a generic input-output sample. Environ. Model Softw. 2018;108:197–207. doi: 10.1016/j.envsoft.2018.07.019. DOI
Baroni G., Francke T. An effective strategy for combining variance- and distribution-based global sensitivity analysis. Environ. Modell. Softw. 2020;134:104851. doi: 10.1016/j.envsoft.2020.104851. DOI
Krykacz-Hausmann B. Epistemic sensitivity analysis based on the concept of entropy; Proceedings of the International Symposium on Sensitivity Analysis of Model Output; Madrid, Spain. 18–20 June 2001; pp. 31–35.
Shannon C.E. A Mathematical theory of communication. Bell Syst. Tech. J. 1948;27:379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x. DOI
Liu H., Sudjianto A., Chen W. Relative entropy based method for probabilistic sensitivity analysis in engineering design. J. Mech. Des. 2006;128:326–336. doi: 10.1115/1.2159025. DOI
Zhong R.X., Fu K.Y., Sumalee A., Ngoduy D., Lam W.H.K. A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models. Transp. Res. Part C Emerg. Technol. 2016;63:147–169. doi: 10.1016/j.trc.2015.12.006. DOI
Tang Z.C., Lu Z.Z., Pan W., Zhang F. An entropy-based global sensitivity analysis for the structures with both fuzzy variables and random variables. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 2013;227:195–212.
Shi Y., Lu Z., Zhou Y. Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy. Eng. Optim. 2018;50:1078–1096. doi: 10.1080/0305215X.2017.1359585. DOI
Yazdani A., Nicknam A., Dadras E.Y., Eftekhari S.N. Entropy-based sensitivity analysis of global seismic demand of concrete structures. Eng. Struct. 2017;146:118–126. doi: 10.1016/j.engstruct.2017.05.023. DOI
Zeng X., Wang D., Wu J. Sensitivity analysis of the probability distribution of groundwater level series based on information entropy. Stoch. Environ. Res. Risk Assess. 2012;26:345–356. doi: 10.1007/s00477-012-0556-2. DOI
Zhu G.R., Wang X.H., Huang H.B., Chen H. Sensitivity analysis for shell-and-tube heat exchangers based on entropy production. Adv. Mat. Res. 2012;516–517:419–424. doi: 10.4028/www.scientific.net/AMR.516-517.419. DOI
Tanyimboh T.T., Setiadi Y. Sensitivity analysis of entropy-constrained designs of water distribution systems. Eng. Optim. 2008;40:439–457. doi: 10.1080/03052150701804571. DOI
Lashkar-Ara B., Kalantari N., Sheikh Khozani Z., Mosavi A. Assessing machine learning versus a mathematical model to estimate the transverse shear stress distribution in a rectangular channel. Mathematics. 2021;9:596. doi: 10.3390/math9060596. DOI
Zhou C., Cui G., Liang W., Liu Z., Zhang L. A coupled macroscopic and mesoscopic creep model of soft marine soil using a directional probability entropy approach. J. Mar. Sci. Eng. 2021;9:224. doi: 10.3390/jmse9020224. DOI
Pan P., Zhang M., Peng W., Chen H., Xu G., Liu T. Thermodynamic evaluation and sensitivity analysis of a novel compressed air energy storage system incorporated with a coal-fired power plant. Entropy. 2020;22:1316. doi: 10.3390/e22111316. PubMed DOI PMC
Lescauskiene I., Bausys R., Zavadskas E.K., Juodagalviene B. VASMA weighting: Survey-based criteria weighting methodology that combines ENTROPY and WASPAS-SVNS to reflect the psychometric features of the VAS scales. Symmetry. 2020;12:1641. doi: 10.3390/sym12101641. DOI
Hashemi H., Mousavi S.M., Zavadskas E.K., Chalekaee A., Turskis Z. A New group decision model based on grey-intuitionistic fuzzy-ELECTRE and VIKOR for contractor assessment problem. Sustainability. 2018;10:1635. doi: 10.3390/su10051635. DOI
Cavallaro F., Zavadskas E.K., Raslanas S. Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS. Sustainability. 2016;8:556. doi: 10.3390/su8060556. DOI
Ghorabaee M.K., Zavadskas E.K., Amiri M., Esmaeili A. Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. J. Clean. Prod. 2016;137:213–229. doi: 10.1016/j.jclepro.2016.07.031. DOI
Liu D., Luo Y., Liu Z. The linguistic picture fuzzy set and its application in multi-criteria decision-making: An illustration to the TOPSIS and TODIM methods based on entropy weight. Symmetry. 2020;12:1170. doi: 10.3390/sym12071170. DOI
Maume-Deschamps V., Niang I. Estimation of quantile oriented sensitivity indices. Stat. Probab. Lett. 2018;134:122–127. doi: 10.1016/j.spl.2017.10.019. DOI
Kucherenko S., Song S., Wang L. Quantile based global sensitivity measures. Reliab. Eng. Syst. Saf. 2019;185:35–48253. doi: 10.1016/j.ress.2018.12.001. DOI
Kala Z. Quantile-oriented global sensitivity analysis of design resistance. J. Civ. Eng. Manag. 2019;25:297–305. doi: 10.3846/jcem.2019.9627. DOI
Kala Z. Quantile-based versus Sobol sensitivity analysis in limit state design. Structures. 2020;28:2424–2430. doi: 10.1016/j.istruc.2020.10.037. DOI
Kala Z. From probabilistic to quantile-oriented sensitivity analysis: New indices of design quantiles. Symmetry. 2020;12:1720. doi: 10.3390/sym12101720. DOI
Kala Z. Global sensitivity analysis of quantiles: New importance measure based on superquantiles and subquantiles. Symmetry. 2021;13:263. doi: 10.3390/sym13020263. DOI
Wei P., Lu Z., Hao W., Feng J., Wang B. Efficient sampling methods for global reliability sensitivity analysis. Comput. Phys. Commun. 2012;183:1728–1743. doi: 10.1016/j.cpc.2012.03.014. DOI
Zhao J., Zeng S., Guo J., Du S. Global reliability sensitivity analysis based on maximum entropy and 2-Layer polynomial chaos expansion. Entropy. 2018;20:202. doi: 10.3390/e20030202. PubMed DOI PMC
Zhang X., Liu J., Yan Y., Pandey M. An effective approach for reliability-based sensitivity analysis with the principle of Maximum entropy and fractional moments. Entropy. 2019;21:649. doi: 10.3390/e21070649. PubMed DOI PMC
Kala Z. Global sensitivity analysis of reliability of structural bridge system. Eng. Struct. 2019;194:36–45. doi: 10.1016/j.engstruct.2019.05.045. DOI
Kala Z. Estimating probability of fatigue failure of steel structures. Acta Comment. Univ. Tartu. Math. 2019;23:245–254. doi: 10.12697/ACUTM.2019.23.21. DOI
Kala Z. Sensitivity analysis in probabilistic structural design: A comparison of selected techniques. Sustainability. 2020;12:4788. doi: 10.3390/su12114788. DOI
Lei J., Lu Z., He L. The single-loop Kriging model combined with Bayes’ formula for time-dependent failure probability based global sensitivity. Structures. 2021;32:987–996. doi: 10.1016/j.istruc.2021.03.019. DOI
Wang P., Li H., Huang X., Zhang Z., Xiao S. Numerical decomposition for the reliability-oriented sensitivity with dependent variables using vine copulas. J. Mech. Des. 2021;143:081701. doi: 10.1115/1.4048961. DOI
Rani P., Mishra A.R., Mardani A., Cavallaro F., Štreimikienė D., Khan S.A.R. Pythagorean Fuzzy SWARA–VIKOR Framework for Performance Evaluation of Solar Panel Selection. Sustainability. 2020;12:4278. doi: 10.3390/su12104278. DOI
Mitrović Simić J., Stević Ž., Zavadskas E.K., Bogdanović V., Subotić M., Mardani A. A Novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS model for safety evaluation of road sections based on geometric parameters of road. Symmetry. 2020;12:2006. doi: 10.3390/sym12122006. DOI
Rani P., Mishra A.R., Krishankumar R., Mardani A., Cavallaro F., Soundarapandian Ravichandran K., Balasubramanian K. Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS) Symmetry. 2020;12:1152. doi: 10.3390/sym12071152. DOI
Puška A., Nedeljković M., Hashemkhani Zolfani S., Pamučar D. Application of interval fuzzy logic in selecting a sustainable supplier on the example of agricultural production. Symmetry. 2021;13:774. doi: 10.3390/sym13050774. DOI
Wang A., Solomatine D.P. Practical experience of sensitivity analysis: Comparing six methods, on three hydrological models, with three performance criteria. Water. 2019;11:1062. doi: 10.3390/w11051062. DOI
Štefaňák J., Kala Z., Miča L., Norkus A. Global sensitivity analysis for transformation of Hoek-Brown failure criterion for rock mass. J. Civ. Eng. Manag. 2018;24:390–398. doi: 10.3846/jcem.2018.5194. DOI
Ching D.S., Safta C., Reichardt T.A. Sensitivity-informed bayesian inference for home PLC network models with unknown parameters. Energies. 2021;14:2402. doi: 10.3390/en14092402. DOI
Rahn S., Gödel M., Fischer R., Köster G. Dynamics of a simulated demonstration march: An efficient sensitivity analysis. Sustainability. 2021;13:3455. doi: 10.3390/su13063455. DOI
Martínez-Ruiz A., Ruiz-García A., Prado-Hernández J.V., López-Cruz I.L., Valencia-Islas J.O., Pineda-Pineda J. Global sensitivity analysis and calibration by differential evolution algorithm of HORTSYST crop model for fertigation management. Water. 2021;13:610. doi: 10.3390/w13050610. DOI
Xu N., Luo J., Zuo J., Hu X., Dong J., Wu T., Wu S., Liu H. Accurate suitability evaluation of large-scale roof greening based on RS and GIS methods. Sustainability. 2020;12:4375. doi: 10.3390/su12114375. DOI
Islam A.B.M., Karadoğan E. Analysis of one-dimensional ivshin–pence shape memory alloy constitutive model for sensitivity and uncertainty. Materials. 2020;13:1482. doi: 10.3390/ma13061482. PubMed DOI PMC
Gamannossi A., Amerini A., Mazzei L., Bacci T., Poggiali M., Andreini A. Uncertainty quantification of film cooling performance of an industrial gas turbine vane. Entropy. 2020;22:16. doi: 10.3390/e22010016. PubMed DOI PMC
De Falco A., Resta C., Sevieri G. Sensitivity analysis of frequency-based tie-rod axial load evaluation methods. Eng. Struct. 2021;229:111568. doi: 10.1016/j.engstruct.2020.111568. DOI
Antucheviciene J., Kala Z., Marzouk M., Vaidogas E.R. Solving civil engineering problems by means of fuzzy and stochastic MCDM methods: Current state and future research. Math. Probl. Eng. 2015;2015:362579. doi: 10.1155/2015/362579. DOI
Kala Z., Valeš J. Sensitivity assessment and lateral-torsional buckling design of I-beams using solid finite elements. J. Constr. Steel Res. 2017;139:110–122. doi: 10.1016/j.jcsr.2017.09.014. DOI
Wen Z., Xia Y., Ji Y., Liu Y., Xiong Z., Lu H. Study on risk control of water inrush in tunnel construction period considering uncertainty. J. Civ. Eng. Manag. 2019;25:757–772. doi: 10.3846/jcem.2019.10394. DOI
Strieška M., Koteš P. Sensitivity of dose-response function for carbon steel under various conditions in Slovakia. Transp. Res. Procedia. 2019;40:912–919. doi: 10.1016/j.trpro.2019.07.128. DOI
Su L., Wang T., Li H., Chao Y., Wang L. Multi-criteria decision making for identification of unbalanced bidding. J. Civ. Eng. Manag. 2020;26:43–52. doi: 10.3846/jcem.2019.11568. DOI
Rykov V., Kozyrev D. On the reliability function of a double redundant system with general repair time distribution. Appl. Stoch. Models Bus Ind. 2019;35:191–197. doi: 10.1002/asmb.2368. DOI
Luo L., Zhang L., Wu G. Bayesian belief network-based project complexity measurement considering causal relationships. J. Civ. Eng. Manag. 2020;26:200–2015. doi: 10.3846/jcem.2020.11930. DOI
Strauss A., Moser T., Honeger C., Spyridis P., Frangopol D.M. Likelihood of impact events in transport networks considering road conditions, traffic and routing elements properties. J. Civ. Eng. Manag. 2020;26:95–112. doi: 10.3846/jcem.2020.11826. DOI
Rykov V.V., Sukharev M.G., Itkin V.Y. Investigations of the potential application of k-out-of-n systems in oil and gas industry objects. J. Mar. Sci. Eng. 2020;8:928. doi: 10.3390/jmse8110928. DOI
Pan L., Novák L., Lehký D., Novák D., Cao M. Neural network ensemble-based sensitivity analysis in structural engineering: Comparison of selected methods and the influence of statistical correlation. Comput. Struct. 2021;242:106376. doi: 10.1016/j.compstruc.2020.106376. DOI
Schroeder M.J. An Alternative to entropy in the measurement of information. Entropy. 2004;6:388–412. doi: 10.3390/e6050388. DOI
Kullback S., Leibler R. On information and sufficiency. Ann. Math. Stat. 1951;22:79–86. doi: 10.1214/aoms/1177729694. DOI
Kullback S. Information Theory and Statistics. John Wiley and Sons; Hoboken, NJ, USA: 1959.
Gamboa F., Klein T., Lagnoux A. Sensitivity analysis based on Cramér-von Mises distance. SIAM/ASA J. Uncertain. Quantif. 2018;6:522–548. doi: 10.1137/15M1025621. DOI
Kala Z. Limit states of structures and global sensitivity analysis based on Cramér-von Mises distance. Int. J. Mech. 2020;14:107–118.
Borgonovo E. A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 2007;92:771–784. doi: 10.1016/j.ress.2006.04.015. DOI
Kala Z. Sensitivity assessment of steel members under compression. Eng. Struct. 2009;31:1344–1348. doi: 10.1016/j.engstruct.2008.04.001. DOI
Kala Z. Global sensitivity analysis in stability problems of steel frame structures. J. Civ. Eng. Manag. 2016;22:417–424. doi: 10.3846/13923730.2015.1073618. DOI
Kala Z., Valeš J. Imperfection sensitivity analysis of steel columns at ultimate limit state. Arch. Civ. Mech. Eng. 2018;18:1207–1218. doi: 10.1016/j.acme.2018.01.009. DOI
Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S. Global Sensitivity Analysis: The Primer. John Wiley & Sons; West Sussex, UK: 2008.
Melcher J., Kala Z., Holický M., Fajkus M., Rozlívka L. Design characteristics of structural steels based on statistical analysis of metallurgical products. J. Constr. Steel Res. 2004;60:795–808. doi: 10.1016/S0143-974X(03)00144-5. DOI
Kala Z., Melcher J., Puklický L. Material and geometrical characteristics of structural steels based on statistical analysis of metallurgical products. J. Civ. Eng. Manag. 2009;15:299–307. doi: 10.3846/1392-3730.2009.15.299-307. DOI
McKey M.D., Beckman R.J., Conover W.J. A comparison of the three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics. 1979;21:239–245.
Iman R.C., Conover W.J. Small sample sensitivity analysis techniques for computer models with an application to risk assessment. Commun. Stat. Theory Methods. 1980;9:1749–1842. doi: 10.1080/03610928008827996. DOI