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A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia

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Abstract

Real-time and short-term prediction of river flow is essential for efficient flood management. To obtain accurate flow predictions, a reliable rainfall-runoff model must be used. This study proposes the application of two evolutionary algorithms, particle swarm optimization (PSO) and genetic algorithm (GA), to train the artificial neural network (ANN) parameters in order to overcome the ANN drawbacks, such as slow learning speed and frequent trapping at local optimum. These hybrid ANN-PSO and ANN-GA approaches were validated to equip natural hazard decision makers with a robust tool for forecasting real-time streamflow as a function of combinations of different lagged rainfall and streamflow in a small catchment in Southeast Queensland, Australia. Different input combinations of lagged rainfall and streamflow (delays of one, two and three days) were tested to investigate the sensitivity of the model to the number of delayed days, and to identify the effective model input combinations for the accurate prediction of real-time streamflow, which has not yet been recognized in other studies. The results indicated that the ANN-PSO model significantly outperformed the ANN-GA model in terms of convergence speed, accuracy, and fitness function evaluation. Additionally, it was found that the rainfall and streamflow with 3-day lag time had less impact on the predicted streamflow of the studied basin, confirming that the flow of the studied river is significantly correlated with only 2-day lagged rainfall and streamflow.

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Notes

  1. Nedbor Affstromnings Model.

  2. Hydrologiska Byråns Vattenbalansavdelning.

  3. Systeme Hydrologique Europeen.

  4. Soil and Water Assessment Tool.

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Acknowledgements

Funding for this project has been provided by Griffith University Postgraduate Research School through the GUPRS scholarship, and Griffith University International Postgraduate Research School through the GUIPRS scholarship. The authors would also like to acknowledge the support of the Water Monitoring Information Portal (WMIP), Queensland Government (Australia) in their provision of the streamflow data, and the Bureau of Meteorology (Australia) for providing the rainfall data.

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  1. School of Engineering and Built Environment, Gold Coast Campus, Griffith University, Gold Coast, QLD, 4222, Australia

    Mahsa Jahandideh-Tehrani, Graham Jenkins & Fernanda Helfer

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  1. Mahsa Jahandideh-Tehrani
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  2. Graham Jenkins
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  3. Fernanda Helfer
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Correspondence to Mahsa Jahandideh-Tehrani.

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Jahandideh-Tehrani, M., Jenkins, G. & Helfer, F. A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia. Optim Eng 22, 29–50 (2021). https://doi.org/10.1007/s11081-020-09538-3

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