Efficient uncertainty quantification for seawater intrusion prediction using Optimized sampling and Null Space Monte Carlo method

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

“…Statistical methods dedicated to the broad field of uncertainty analysis can be divided into six classes: Monte Carlo sampling, response surface-based methods including polynomial chaos expansion and machine learning, multi-model approaches, Bayesian statistics, multi-criteria analysis, and least-squares-based inverse modelling [614]. Methods successfully applied in water-related studies include generalised likelihood uncertainty estimation (GLUE) [616,617], differential adaptive evolution (DREAM) [532,618], parameter estimation code (PEST) [619,620], the Bayesian approach including total error analysis (BATEA) [621][622][623][624][625] and multiobjective analysis [626,627], machine learning methods [628], and the Null-Space Monte Carlo method (NSMC) [629,630]. Some helpful guidelines for successful calibration and uncertainty analysis, even under the pressure of climate change, can be found in the work of Mai [631].…”