Application of a Genetic Algorithm in the Development and Optimisation of a Non-linear Dynamic Runoff Model

Abstract

Daily rainfall and runoff data for the Kashinagar watershed of the Vamsadhara river basin, Orissa, India collected for the years 1984–1995 have been used for the development and optimisation of a runoff prediction model. The prediction model has been developed by considering the process as non-linear and dynamic in nature. Model orders are determined by multiple correlation analysis and then based on ‘ t-test’ values. All positive values of the t-test, obtained through multiple correlation, are considered for model formulation. Genetic algorithm has been employed for the estimation of model parameters and function optimisation. The predicted values by the non-linear rainfall–runoff prediction model were 1–2 days ahead of the measured values. This time problem was corrected by applying backward shift operator technique which improved the correlation coefficient by about 15%.