English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Solving hydrologic inverse problems usually requires repetitive forwardsimulations. One approach to mitigate the computational cost is to build asurrogate model, i.e., an approximate mapping from model parameters (input) toobservable quantities (output), so the forward simulations can be done quickly.Alternatively, if the surrogate is constructed to approximate the inversemapping from model outputs to parameters, the parameter estimates can beobtained directly by treating measurements as inputs to this inverse surrogate.Moreover, the uncertainties of parameters can be quantified by propagating themeasurement uncertainties in a straightforward Monte Carlo manner. Based onthis idea, we proposed a novel surrogate-based approach for parameterestimation and uncertainty assessment, i.e., the Inverse Gaussian Process MonteCarlo (IGPMC) algorithm. The Gaussian Process (GP) regression is used todirectly approximate the inverse function of the model output-inputrelationship. For ill-posed problems, i.e., when there exist non-unique sets ofinput parameters that all produce an identical system output, multiple inverseGP systems are constructed and multiple parameter estimates can be obtainedaccordingly. The accuracy and efficiency of this IGPMC algorithm weredemonstrated through four numerical case studies. Results obtained from theMarkov Chain Monte Carlo (MCMC) are used as references to assess our newproposed method. It was shown that, the IGPMC algorithm can generally obtainreliable parameter estimates with an affordable computational cost.

An Inverse Gaussian Process Monte Carlo algorithm for estimation and uncertainty assessment of hydrologic model parameters