| 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.
Keywords: parameters, calibration, GLUE, Monte-Carlo simulation, Genetic Algorithms,
Artificial Neural Networks, hydrological modelling, Singapore |