University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

Khu, ST and Werner, MGF (2003) Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling Hydrology and Earth System Sciences, 7 (5). pp. 680-692.

Full text not available from this repository.

Abstract

Monte-Carlo (MC) simulation based techniques are often applied for the estimation of uncertainties in hydrological models due to uncertain parameters. One such technique is the Generalised Likelihood Uncertainty Estimation technique (GLUE). A major disadvantage of MC is the large number of runs required to establish a reliable estimate of model uncertainties. To reduce the number of runs required, a hybrid genetic algorithm and artificial neural network, known as GAANN, is applied. In this method, GA is used to identify the area of importance and ANN is used to obtain an initial estimate of the model performance by mapping the response surface. Parameter sets which give non-behavioural model runs are discarded before running the hydrological model, effectively reducing the number of actual model runs performed. The proposed method is applied to the case of a simple two-parameter model where the exact parameters are known as well as to a widely used catchment model where the parameters are to be estimated. The results of both applications indicated that the proposed method is more efficient and effective, thereby requiring fewer model simulations than GLUE. The proposed method increased the feasibility of applying uncertainty analysis to computationally intensive simulation models. © EGU.

Item Type: Article
Authors :
NameEmailORCID
Khu, STs.khu@surrey.ac.ukUNSPECIFIED
Werner, MGFUNSPECIFIEDUNSPECIFIED
Date : 1 October 2003
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:53
Last Modified : 17 May 2017 11:53
URI: http://epubs.surrey.ac.uk/id/eprint/833128

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800