RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia
Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relation...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yaseen, Zaher Mundher [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© The Natural Computing Applications Forum 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 27(2015), 6 vom: 28. Juni, Seite 1533-1542 |
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| Übergeordnetes Werk: |
volume:27 ; year:2015 ; number:6 ; day:28 ; month:06 ; pages:1533-1542 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-015-1952-6 |
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OLC2025598645 |
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| 520 | |a Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. | ||
| 650 | 4 | |a Streamflow forecasting | |
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| 700 | 1 | |a Hameed, Mohammed |4 aut | |
| 700 | 1 | |a Mohtar, Wan Hanna Melini Wan |4 aut | |
| 700 | 1 | |a Hussain, Aini |4 aut | |
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10.1007/s00521-015-1952-6 doi (DE-627)OLC2025598645 (DE-He213)s00521-015-1952-6-p DE-627 ger DE-627 rakwb eng 004 VZ Yaseen, Zaher Mundher verfasserin aut RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2015 Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. Streamflow forecasting Artificial neural networks FFNN RBFNN El-Shafie, Ahmed aut Afan, Haitham Abdulmohsin aut Hameed, Mohammed aut Mohtar, Wan Hanna Melini Wan aut Hussain, Aini aut Enthalten in Neural computing & applications Springer London, 1993 27(2015), 6 vom: 28. Juni, Seite 1533-1542 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:27 year:2015 number:6 day:28 month:06 pages:1533-1542 https://doi.org/10.1007/s00521-015-1952-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 27 2015 6 28 06 1533-1542 |
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10.1007/s00521-015-1952-6 doi (DE-627)OLC2025598645 (DE-He213)s00521-015-1952-6-p DE-627 ger DE-627 rakwb eng 004 VZ Yaseen, Zaher Mundher verfasserin aut RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2015 Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. Streamflow forecasting Artificial neural networks FFNN RBFNN El-Shafie, Ahmed aut Afan, Haitham Abdulmohsin aut Hameed, Mohammed aut Mohtar, Wan Hanna Melini Wan aut Hussain, Aini aut Enthalten in Neural computing & applications Springer London, 1993 27(2015), 6 vom: 28. Juni, Seite 1533-1542 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:27 year:2015 number:6 day:28 month:06 pages:1533-1542 https://doi.org/10.1007/s00521-015-1952-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 27 2015 6 28 06 1533-1542 |
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10.1007/s00521-015-1952-6 doi (DE-627)OLC2025598645 (DE-He213)s00521-015-1952-6-p DE-627 ger DE-627 rakwb eng 004 VZ Yaseen, Zaher Mundher verfasserin aut RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2015 Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. Streamflow forecasting Artificial neural networks FFNN RBFNN El-Shafie, Ahmed aut Afan, Haitham Abdulmohsin aut Hameed, Mohammed aut Mohtar, Wan Hanna Melini Wan aut Hussain, Aini aut Enthalten in Neural computing & applications Springer London, 1993 27(2015), 6 vom: 28. Juni, Seite 1533-1542 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:27 year:2015 number:6 day:28 month:06 pages:1533-1542 https://doi.org/10.1007/s00521-015-1952-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 27 2015 6 28 06 1533-1542 |
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10.1007/s00521-015-1952-6 doi (DE-627)OLC2025598645 (DE-He213)s00521-015-1952-6-p DE-627 ger DE-627 rakwb eng 004 VZ Yaseen, Zaher Mundher verfasserin aut RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2015 Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. Streamflow forecasting Artificial neural networks FFNN RBFNN El-Shafie, Ahmed aut Afan, Haitham Abdulmohsin aut Hameed, Mohammed aut Mohtar, Wan Hanna Melini Wan aut Hussain, Aini aut Enthalten in Neural computing & applications Springer London, 1993 27(2015), 6 vom: 28. Juni, Seite 1533-1542 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:27 year:2015 number:6 day:28 month:06 pages:1533-1542 https://doi.org/10.1007/s00521-015-1952-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 27 2015 6 28 06 1533-1542 |
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RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia |
| abstract |
Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. © The Natural Computing Applications Forum 2015 |
| abstractGer |
Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. © The Natural Computing Applications Forum 2015 |
| abstract_unstemmed |
Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting. © The Natural Computing Applications Forum 2015 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 |
| container_issue |
6 |
| title_short |
RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia |
| url |
https://doi.org/10.1007/s00521-015-1952-6 |
| remote_bool |
false |
| author2 |
El-Shafie, Ahmed Afan, Haitham Abdulmohsin Hameed, Mohammed Mohtar, Wan Hanna Melini Wan Hussain, Aini |
| author2Str |
El-Shafie, Ahmed Afan, Haitham Abdulmohsin Hameed, Mohammed Mohtar, Wan Hanna Melini Wan Hussain, Aini |
| ppnlink |
165669608 |
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| isOA_txt |
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| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-015-1952-6 |
| up_date |
2024-07-04T01:39:39.466Z |
| _version_ |
1803610677016788992 |
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7.4010296 |