Use of a supercomputer to advance parameter optimisation using genetic algorithms

Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 computer are used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 390 computer and an IBM 9076 SP2 supercomputer reveals that the latter can be up to 8 times faster. The effectiveness of the modified GA is tested with two Tank Models for a hypothetical catchment and a real catchment. The former showed that the parallel GA reaches a lower overall error in reduced time. The overall RMSE, expressed as a percentage of actual mean flow rate, improves from 31.8% in a serial processing computer to 29.5% on the SP2 supercomputer. The case of the real catchment – Shek-Pi-Tau Catchment in Hong Kong – reveals that the supercomputer enhances the swiftness of the GA and achieves its objective within a couple of hours.