Presenter/Author Information

Soon-Thiam Khu
Edward Keedwell
Oliver Pollard

Keywords

genetic programming, real-time updating, rainfall-runoff, artificial neural networks, forecasting

Start Date

1-7-2004 12:00 AM

Description

Error-correction is widely known to be one of the effective methods of real-time updating and tends to be the easiest method to implement and couple with existing simulation models. Methods such as autoregressive (AR) or autoregressive integrated moving average (ARIMA) have been widely used but the main disadvantage of such approaches is the prior assumption of the form of error correlation. Genetic programming (GP), a relatively new evolutionary-based technique, can be used to generate a suitable expression linking the observations, simulation model results and the error in the simulation for the purpose of error correction. In this study, GP functions as an error correction scheme to complement runoff forecasting model used by the UK Environment Agency (Southwest region) known as WRIP. WRIP is a transfer function-based operational forecasting software which uses radar rainfall as input. The proposed method is tested on a flashy catchment in Devon, UK. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to six hours. The results show that the proposed updating scheme is able to forecast the runoff quite accurately for all updating intervals considered and particularly for those updating intervals not exceeding the time of concentration of the catchment. These results formed part of an ongoing feasibility studies by the UK Environment Agency and the proposed method will be tested on other catchments in the future.

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Jul 1st, 12:00 AM

An Evolutionary-based Real-time Updating Technique for an Operational Rainfall-Runoff Forecasting Model

Error-correction is widely known to be one of the effective methods of real-time updating and tends to be the easiest method to implement and couple with existing simulation models. Methods such as autoregressive (AR) or autoregressive integrated moving average (ARIMA) have been widely used but the main disadvantage of such approaches is the prior assumption of the form of error correlation. Genetic programming (GP), a relatively new evolutionary-based technique, can be used to generate a suitable expression linking the observations, simulation model results and the error in the simulation for the purpose of error correction. In this study, GP functions as an error correction scheme to complement runoff forecasting model used by the UK Environment Agency (Southwest region) known as WRIP. WRIP is a transfer function-based operational forecasting software which uses radar rainfall as input. The proposed method is tested on a flashy catchment in Devon, UK. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to six hours. The results show that the proposed updating scheme is able to forecast the runoff quite accurately for all updating intervals considered and particularly for those updating intervals not exceeding the time of concentration of the catchment. These results formed part of an ongoing feasibility studies by the UK Environment Agency and the proposed method will be tested on other catchments in the future.