Abstract
Gaussian process emulation (GPE) and polynomial chaos expansion (PCE) are tools for meta-model-based uncertainty propagation analysis (UPA) that have gained increasing attention in recent groundwater literature. Previous studies have shown that these two meta-models can provide satisfactorily accurate estimations of the model response in a wide range of groundwater UPA problems. However, PCE and GPE are based on very different mathematical concepts, and a question that arises is which one of these is more suitable for groundwater UPA. The current paper aims to provide an answer to this question by first presenting a theoretical comparison of the two meta-models, then reviewing previous comparisons of the two in other fields of engineering, and subsequently presenting an empirical comparison based on groundwater case studies. For this purpose, both meta-models are applied to two hypothetical test cases corresponding to seawater intrusion in coastal aquifers. The results show that: (1) GPE outperforms PCE in the estimation of the input–output relationships, by providing smaller root mean square errors. (2) In most cases assessed, PCE provides better accuracy in the estimation of mean, standard deviation and the entire shape and the tail of probability distribution functions. (3) Replicates of PCE show less statistical dispersion in the estimation of mean and standard deviations. (4) A trend of increase in the predominance of PCE over GPE can be identified as the probability distributions that are meant to be estimated become noisier and more multi-modal.
Similar content being viewed by others
References
Alcolea A, Carrera J, Medina A (2006) Pilot points method incorporating prior information for solving the groundwater flow inverse problem. Adv Water Resour 29(11):1678–1689
Asher MJ, Croke BFW, Jakeman AJ, Peeters LJM (2015) A review of surrogate models and their application to groundwater modeling. Water Resour Res 51(8):5957–5973
Ataie-Ashtiani B, Ketabchi H, Rajabi MM (2013) Optimal management of a freshwater lens in a small island using surrogate models and evolutionary algorithms. J Hydrol Eng 19(2):339–354
Babuška I, Nobile F, Tempone R (2010) A stochastic collocation method for elliptic partial differential equations with random input data. SIAM Rev 52(2):317–355
Baú DA, Mayer AS (2006) Stochastic management of pump-and-treat strategies using surrogate functions. Adv Water Resour 29(12):1901–1917
Berveiller M, Sudret B, Lemaire M (2006) Stochastic finite element: a non intrusive approach by regression. Eur J Comput Mech Revue Européenne de Mécanique Numérique 15(1–3):81–92
Blatman G, Sudret B (2010) An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probab Eng Mech 25(2):183–197
Blatman G, Sudret B (2011) Adaptive sparse polynomial chaos expansion based on least angle regression. J Comput Phys 230(6):2345–2367
Burrows W, Doherty J (2015) Efficient calibration/uncertainty analysis using paired complex/surrogate models. Groundwater 53(4):531–541
Busby D (2009) Hierarchical adaptive experimental design for Gaussian process emulators. Reliab Eng Syst Saf 94(7):1183–1193
Cohen A, Devore R, Schwab C (2010) Convergence rates of best N-term Galerkin approximations for a class of elliptic sPDEs. Found Comput Math 10(6):615–646
Cohen A, Devore R, Schwab C (2011) Analytic regularity and polynomial approximation of parametric and stochastic elliptic PDE’s. Anal Appl 9(01):11–47
Conti S, O’Hagan A (2010) Bayesian emulation of complex multi-output and dynamic computer models. J Stat Plan Inference 140(3):640–651
Cornaton FJ (2007) Ground water: a 3-D ground water and surface water flow, mass transport and heat transfer finite element simulator, reference manual. University of Neuchâtel, Neuchâtel, p 418
Crevillen-Garcia D (2016) Uncertainty quantification for flow and transport in porous media. Doctoral dissertation, University of Nottingham
Crevillen-Garcia D, Wilkinson RD, Shah AA, Power H (2017) Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media. Adv Water Resour 99:1–14
Deman G, Konakli K, Sudret B, Kerrou J, Perrochet P, Benabderrahmane H (2016) Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model. Reliab Eng Syst Saf 147:156–169
Doherty J (2003) Ground water model calibration using pilot points and regularization. Groundwater 41(2):170–177
Dupuy D, Helbert C, Franco J (2015) DiceDesign and DiceEval: two R packages for design and analysis of computer experiments. J Stat Softw 65(11):1–38
Ebden M (2008) Gaussian processes for regression: a quick introduction. The Website of Robotics Research Group in Department on Engineering Science, University of Oxford, Oxford
Eldred M, Webster C, Constantine P (2008) Evaluation of non-intrusive approaches for Wiener–Askey generalized polynomial chaos. In: 49th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, 16th AIAA/ASME/AHS adaptive structures conference, 10th AIAA non-deterministic approaches conference, 9th AIAA Gossamer spacecraft forum, 4th AIAA multidisciplinary design optimization specialists conference, p 1892
Fang KT, Ma CX, Winker P (2002) Centered L 2-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs. Math Comput 71(237):275–296
Feil B, Kucherenko S, Shah N (2009) Comparison of Monte Carlo and quasi Monte Carlo sampling methods in high dimensional model representation. In: 1st International conference on advances in system simulation, 2009. SIMUL’09. IEEE, pp 12–17
Feinberg J, Langtangen HP (2015) Chaospy: an open source tool for designing methods of uncertainty quantification. J Comput Sci 11:46–57
Fricker TE, Oakley JE, Sims ND, Worden K (2011) Probabilistic uncertainty analysis of an FRF of a structure using a Gaussian process emulator. Mech Syst Signal Process 25(8):2962–2975
Ghanem R (1998) Probabilistic characterization of transport in heterogeneous media. Comput Methods Appl Mech Eng 158(3–4):199–220
Ghanem RG, Spanos PD (eds) (1991) Stochastic finite element method: response statistics. In: Stochastic finite elements: a spectral approach. Springer, New York, pp 101–119
Ghanem R, Higdon D, Owhadi H (2017) Handbook of uncertainty quantification. Springer, New York
Ghiocel DM, Ghanem RG (2002) Stochastic finite-element analysis of seismic soil–structure interaction. J Eng Mech 128(1):66–77
Gratiet LL, Marelli S, Sudret B (2017) Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes. In: Ghanem R, Higdon D, Owhadi H (eds) Handbook of uncertainty quantification. Springer, Cham, pp 1289–1325
Haji-Ali AL, Nobile F, Tamellini L, Tempone R (2016) Multi-index stochastic collocation for random PDEs. Comput Methods Appl Mech Eng 306:95–122
Hayley K (2017) The present state and future application of cloud computing for numerical groundwater modeling. Groundwater 55(5):678–682
Henry HR (1964) Effects of dispersion on salt encroachment in coastal aquifers, in” seawater in coastal aquifers”. US Geol Surv Water Supply Pap 1613:C70–C80
Herckenrath D, Langevin CD, Doherty J (2011) Predictive uncertainty analysis of a saltwater intrusion model using null-space Monte Carlo. Water Resour Res 47:W05504
Hu C, Youn BD (2011) Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems. Struct Multidiscip Optim 43(3):419–442
Jakeman JD, Narayan A, Xiu D (2013) Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. J Comput Phys 242:790–808
Kennedy MC, O’Hagan A (2000) Predicting the output from a complex computer code when fast approximations are available. Biometrika 87(1):1–13
Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc Ser B (Stat Methodol) 63(3):425–464
Kerrou J, Renard P (2010) A numerical analysis of dimensionality and heterogeneity effects on advective dispersive seawater intrusion processes. Hydrogeol J 18(1):55–72
Kim YJ (2016) Comparative study of surrogate models for uncertainty quantification of building energy model: Gaussian process emulator vs. polynomial chaos expansion. Energy Build 133:46–58
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112
Kumar U, Kumar V, Kapur JN (1986) Normalized measures of entropy. Int J Gen Syst 12(1):55–69
Laloy E, Rogiers B, Vrugt JA, Mallants D, Jacques D (2013) Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion. Water Resour Res 49(5):2664–2682
Le Maître O, Knio OM (2010) Spectral methods for uncertainty quantification: with applications to computational fluid dynamics. Springer, New York
Le Maı̂tre OP, Reagan MT, Najm HN, Ghanem RG, Knio OM (2002) A stochastic projection method for fluid flow: II. Random process. J Comput Phys 181(1):9–44
Li W, Lu Z, Zhang D (2009) Stochastic analysis of unsaturated flow with probabilistic collocation method. Water Resour Res 45:W08425
Liao Q, Zhang D (2016) Probabilistic collocation method for strongly nonlinear problems: 3. Transform by time. Water Resour Res 52(3):2366–2375
Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour Res 43:W07401
Loeppky JL, Sacks J, Welch WJ (2009) Choosing the sample size of a computer experiment: a practical guide. Technometrics 51(4):366–376
Loeppky JL, Moore LM, Williams BJ (2010) Batch sequential designs for computer experiments. J Stat Plan Inference 140(6):1452–1464
Maina FZ, Guadagnini A (2018) Uncertainty quantification and global sensitivity analysis of subsurface flow parameters to gravimetric variations during pumping tests in unconfined aquifers. Water Resour Res 54(1):501–518
Marelli S, Sudret B (2015) UQLab user manual–polynomial chaos expansions. Chair of Risk, Safety & Uncertainty Quantification, ETH Zürich, 0.9-104 edn
McCarthy PC, Sayre JE, Shawyer BLR (1993) Generalized Legendre polynomials. J Math Anal Appl 177(2):530–537
Meng J, Li H (2017) An efficient stochastic approach for flow in porous media via sparse polynomial chaos expansion constructed by feature selection. Adv Water Resour 105:13–28
Migliorati G, Nobile F, von Schwerin E, Tempone R (2013) Approximation of quantities of interest in stochastic PDEs by the random discrete L2 projection on polynomial spaces. SIAM J Sci Comput 35(3):A1440–A1460
O’Hagan A (2006) Bayesian analysis of computer code outputs: a tutorial. Reliab Eng Syst Saf 91(10):1290–1300
O’Hagan A (2013) Polynomial chaos: a tutorial and critique from a statistician’s perspective. SIAM/ASA J Uncertain Quantif 20:1–20
Oladyshkin S, Nowak W (2012) Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliab Eng Syst Saf 106:179–190
Oladyshkin S, Class H, Helmig R, Nowak W (2011) A concept for data-driven uncertainty quantification and its application to carbon dioxide storage in geological formations. Adv Water Resour 34(11):1508–1518
Oughton RH, Craig PS (2016) Hierarchical emulation: a method for modeling and comparing nested simulators. SIAM/ASA J Uncertain Quantif 4(1):495–519
Overstall AM, Woods DC (2017) Bayesian design of experiments using approximate coordinate exchange. Technometrics 59(4):458–470
Owen NE, Challenor P, Menon PP, Bennani S (2017) Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators. SIAM/ASA J Uncertain Quantif 5(1):403–435
Palar PS, Tsuchiya T, Parks GT (2016) Multi-fidelity non-intrusive polynomial chaos based on regression. Comput Methods Appl Mech Eng 305:579–606
Pellissetti MF, Ghanem RG (2000) Iterative solution of systems of linear equations arising in the context of stochastic finite elements. Adv Eng Softw 31(8–9):607–616
Perez RA (2008) Uncertainty analysis of computational fluid dynamics via polynomial chaos. Doctoral dissertation, Virginia Tech
Powell CE, Elman HC (2008) Block-diagonal preconditioning for spectral stochastic finite-element systems. IMA J Numer Anal 29(2):350–375
Rajabi MM, Ataie-Ashtiani B (2014) Sampling efficiency in Monte Carlo based uncertainty propagation strategies: application in seawater intrusion simulations. Adv Water Resour 67:46–64
Rajabi MM, Ataie-Ashtiani B (2016) Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation. J Hydrol 536:255–272
Rajabi MM, Ketabchi H (2017) Uncertainty-based simulation-optimization using Gaussian process emulation: application to coastal groundwater management. J Hydrol 555:518–534
Rajabi MM, Ataie-Ashtiani B, Simmons CT (2015) Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations. J Hydrol 520:101–122
Rasmussen CE, Nickisch H (2010) Gaussian processes for machine learning (GPML) toolbox. J Mach Learn Res 11:3011–3015
Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48:W07401
Roy PT, El Moçayd N, Ricci S, Jouhaud JC, Goutal N, De Lozzo M, Rochoux MC (2018) Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows. Stoch Environ Res Risk Assess 32(6):1723–1741
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. Foundations. United States
Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–423
Saltelli A (2004) Global sensitivity analysis: an introduction. In: Proceedings of 4th international conference on sensitivity analysis of model output (SAMO’04), pp 27–43
Santamaría-Bonfil G, Fernández N, Gershenson C (2016) Measuring the complexity of continuous distributions. Entropy 18(3):72
Schobi R, Sudret B, Wiart J (2015) Polynomial-chaos-based kriging. Int J Uncertain Quantif 5(2):171–193
Sreekanth J, Datta B (2014) Stochastic and robust multi-objective optimal management of pumping from coastal aquifers under parameter uncertainty. Water Resour Manage 28(7):2005–2019
Stone N (2011) Gaussian process emulators for uncertainty analysis in groundwater flow. Doctoral dissertation, University of Nottingham
Sudret B (2008) Global sensitivity analysis using polynomial chaos expansions. Reliab Eng Syst Saf 93(7):964–979
Székely GJ, Rizzo ML (2013) Energy statistics: a class of statistics based on distances. J Stat Plan Inference 143(8):1249–1272
Tian L, Wilkinson R, Yang Z, Power H, Fagerlund F, Niemi A (2017) Gaussian process emulators for quantifying uncertainty in CO2 spreading predictions in heterogeneous media. Comput Geosci 105:113–119
Todor RA, Schwab C (2007) Convergence rates for sparse chaos approximations of elliptic problems with stochastic coefficients. IMA J Numer Anal 27(2):232–261
Vanhatalo J, Riihimäki J, Hartikainen J, Jylänki P, Tolvanen V, Vehtari A (2013) GPstuff: Bayesian modeling with Gaussian processes. J Mach Learn Res 14:1175–1179
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Voss CI, Provost AM (2010) SUTRA, a model for saturated–unsaturated variable density ground-water flow with solute or energy transport. U.S. Geological Survey, water-resources investigations, open-file report 02-4231
Vrugt JA, Nualláin BO, Robinson BA, Bouten W, Dekker SC, Sloot PM (2006) Application of parallel computing to stochastic parameter estimation in environmental models. Comput Geosci 32(8):1139–1155
Wan X, Karniadakis GE (2005) An adaptive multi-element generalized polynomial chaos method for stochastic differential equations. J Comput Phys 209(2):617–642
Welter DE, White JT, Hunt RJ, Doherty JE (2015) Approaches in highly parameterized inversion—PEST++ version 3, a parameter ESTimation and uncertainty analysis software suite optimized for large environmental models (no. 7-C12). US Geological Survey
Wiener N (1938) The homogeneous chaos. Am J Math 60(4):897–936
Williams CK, Rasmussen CE (2006) Gaussian processes for machine learning, vol 2(3). The MIT Press, Cambridge, p 4
Xiu D (2010) Numerical methods for stochastic computations: a spectral method approach. Princeton University Press, Princeton
Xiu D, Hesthaven JS (2005) High-order collocation methods for differential equations with random inputs. SIAM J Sci Comput 27(3):1118–1139
Xiu D, Karniadakis GE (2002) The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput 24(2):619–644
Xiu D, Karniadakis GE (2003) Modeling uncertainty in flow simulations via generalized polynomial chaos. J Comput Phys 187(1):137–167
Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138
Zhang J, Li W, Zeng L, Wu L (2016) An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems. Water Resour Res 52(8):5971–5984
Acknowledgements
The author wishes to thank Editor-in-Chief, Professor George Christakos, Associate Editor and two anonymous reviewers for their valuable comments which helped to improve the final manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rajabi, M.M. Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: polynomial chaos expansion and Gaussian process emulation. Stoch Environ Res Risk Assess 33, 607–631 (2019). https://doi.org/10.1007/s00477-018-1637-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-018-1637-7