Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model
In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models wit...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/67284 |
| _version_ | 1848761526640443392 |
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| author | Badrzadeh, Honey Sarukkalige, Priyantha Ranjan Jayawardena, A. |
| author_facet | Badrzadeh, Honey Sarukkalige, Priyantha Ranjan Jayawardena, A. |
| author_sort | Badrzadeh, Honey |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting. |
| first_indexed | 2025-11-14T10:33:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-67284 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:33:05Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-672842018-09-28T00:52:39Z Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model Badrzadeh, Honey Sarukkalige, Priyantha Ranjan Jayawardena, A. In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting. 2018 Journal Article http://hdl.handle.net/20.500.11937/67284 10.2166/nh.2017.163 restricted |
| spellingShingle | Badrzadeh, Honey Sarukkalige, Priyantha Ranjan Jayawardena, A. Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title | Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title_full | Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title_fullStr | Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title_full_unstemmed | Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title_short | Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| title_sort | intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model |
| url | http://hdl.handle.net/20.500.11937/67284 |