The aim of this paper is to determine the capability of Evolutionary Computation (EC) method in predicting sediment suspension from the velocity field. EC is a robust search and optimisation technique based on the Darwinian principle of natural evolution, where the fittest will survive. There are different algorithms to simulate natural evolution in computer system. In this paper we use Genetic Programming (GP) methodology to explore a better correlation between the temporal pattern of fluid field and sediment transport. GP code developed by Langdon is modified for sediment transport modelling. Two datasets are used, one from numerical model results and other from field data (Sandy Duck), to evolve symbolic expression for sediment transport. The results from the numerical models are encouraging though the GP found it hard to find relationships from the field data.

Calibration is an essential part of the application of conceptual rainfall-streamflow models to watershed management problems for civil engineers. However, the identification of a unique set of parameters is difficult, if not impossible, for commonly used models. Most multiobjective methods and uncertainty assessment tools require substantial numbers of function evaluations and limit the intervention of experienced modelers in the calibration process. This paper demonstrates the application of an efficient user-driven calibration-support system to conceptual rainfall-streamflow models. The system is designed to assist the hydrological modeler by means of rapid sampling of solutions, clustering, and visualization, together with interactivity to exploit the expertise of the user and/or the knowledge revealed by the clustering technique. The efficiency of the multiobjective calibration is enhanced through the use of a novel objective function based on hydrograph slope. The application of the system to the calibration of the SIXPAR conceptual rainfall-streamflow model using a synthetic time series is shown to be effective.