Abstract
It is common knowledge that the optimal values of the calibrated parameters of a rainfall-runoff model for one model response may not be the optimal values for another model response. Thus, it is highly desirable to derive a Pareto front or trade-off curve on which each point represents a set of optimal values satisfying the desirable accuracy levels of each of the model responses. This paper presents a new genetic algorithm (GA) based calibration scheme, accelerated convergence GA (ACGA), which generates a limited number of points on the Pareto front. A neural network (NN) is then trained to compliment ACGA in the derivation of other desired points on the Pareto front by mimicking the relationship between the ACGA-generated calibration parameters and the model responses. The calibration scheme, ACGA, is linked with HydroWorks and tested on a catchment in Singapore. Results show that ACGA is more efficient and effective in deriving the Pareto front compared to other established GA-based optimization techniques such as vector evaluated GA, multiobjective GA, and nondominated sorting GA. Verification of the trained NN forecaster indicates that the trained network reproduces ACGA generated points on the Pareto front accurately. Thus, ACGA-NN is a useful and reliable tool to generate additional points on the Pareto front.