Back to Search
Start Over
Applying ANN emulators in uncertainty assessment of flood inundation modelling: a comparison of two surrogate schemes
- Source :
- Hydrological Sciences Journal. 60:2117-2131
- Publication Year :
- 2015
- Publisher :
- Informa UK Limited, 2015.
-
Abstract
- A generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic i...
- Subjects :
- Mathematical optimization
Artificial neural network
Computer Science::Computational Engineering, Finance, and Science
Computer science
Monte Carlo method
Econometrics
Probabilistic logic
Sampling (statistics)
Prediction interval
Sample (statistics)
GLUE
Uncertainty analysis
Water Science and Technology
Subjects
Details
- ISSN :
- 21503435 and 02626667
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- Hydrological Sciences Journal
- Accession number :
- edsair.doi...........d749feb51466510b7d4740ea4bca0207