Hourly runoff forecasting for flood risk management: Application of various computational intelligence models
© 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 a...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/14053 |
| _version_ | 1848748517186600960 |
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| author | Badrzadeh, H. Sarukkalige, Priyantha Ranjan Jayawardena, A. |
| author_facet | Badrzadeh, H. Sarukkalige, Priyantha Ranjan Jayawardena, A. |
| author_sort | Badrzadeh, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T07:06:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-14053 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:06:18Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-140532017-09-13T15:02:44Z Hourly runoff forecasting for flood risk management: Application of various computational intelligence models Badrzadeh, H. Sarukkalige, Priyantha Ranjan Jayawardena, A. © 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/14053 10.1016/j.jhydrol.2015.07.057 Elsevier restricted |
| spellingShingle | Badrzadeh, H. Sarukkalige, Priyantha Ranjan Jayawardena, A. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title | Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title_full | Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title_fullStr | Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title_full_unstemmed | Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title_short | Hourly runoff forecasting for flood risk management: Application of various computational intelligence models |
| title_sort | hourly runoff forecasting for flood risk management: application of various computational intelligence models |
| url | http://hdl.handle.net/20.500.11937/14053 |