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dc.contributor.authorBadrzadeh, H.
dc.contributor.authorSarukkalige, Priyantha Ranjan
dc.contributor.authorJayawardena, A.
dc.identifier.citationBadrzadeh, H. and Sarukkalige, P.R. and Jayawardena, A. 2015. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models. Journal of Hydrology. 529 (3): pp. 1633-1643.

© 2015 Elsevier B.V. Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48. h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12. h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

dc.titleHourly runoff forecasting for flood risk management: Application of various computational intelligence models
dc.typeJournal Article
dcterms.source.titleJournal of Hydrology
curtin.departmentDepartment of Civil Engineering
curtin.accessStatusFulltext not available

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