Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters
Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pai, T. Y. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media B.V. 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental monitoring and assessment - Springer Netherlands, 1981, 146(2008), 1-3 vom: 15. Jan., Seite 51-66 |
|---|---|
| Übergeordnetes Werk: |
volume:146 ; year:2008 ; number:1-3 ; day:15 ; month:01 ; pages:51-66 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10661-007-0059-7 |
|---|
| Katalog-ID: |
OLC207373071X |
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| 520 | |a Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. | ||
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10.1007/s10661-007-0059-7 doi (DE-627)OLC207373071X (DE-He213)s10661-007-0059-7-p DE-627 ger DE-627 rakwb eng 333.7 VZ Pai, T. Y. verfasserin aut Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2007 Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. Grey model Artificial neural network Wastewater treatment plant Conventional activated sludge process Industrial park Chuang, S. H. aut Wan, T. J. aut Lo, H. M. aut Tsai, Y. P. aut Su, H. C. aut Yu, L. F. aut Hu, H. C. aut Sung, P. J. aut Enthalten in Environmental monitoring and assessment Springer Netherlands, 1981 146(2008), 1-3 vom: 15. Jan., Seite 51-66 (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:146 year:2008 number:1-3 day:15 month:01 pages:51-66 https://doi.org/10.1007/s10661-007-0059-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL GBV_ILN_22 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 146 2008 1-3 15 01 51-66 |
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10.1007/s10661-007-0059-7 doi (DE-627)OLC207373071X (DE-He213)s10661-007-0059-7-p DE-627 ger DE-627 rakwb eng 333.7 VZ Pai, T. Y. verfasserin aut Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2007 Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. Grey model Artificial neural network Wastewater treatment plant Conventional activated sludge process Industrial park Chuang, S. H. aut Wan, T. J. aut Lo, H. M. aut Tsai, Y. P. aut Su, H. C. aut Yu, L. F. aut Hu, H. C. aut Sung, P. J. aut Enthalten in Environmental monitoring and assessment Springer Netherlands, 1981 146(2008), 1-3 vom: 15. Jan., Seite 51-66 (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:146 year:2008 number:1-3 day:15 month:01 pages:51-66 https://doi.org/10.1007/s10661-007-0059-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL GBV_ILN_22 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 146 2008 1-3 15 01 51-66 |
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10.1007/s10661-007-0059-7 doi (DE-627)OLC207373071X (DE-He213)s10661-007-0059-7-p DE-627 ger DE-627 rakwb eng 333.7 VZ Pai, T. Y. verfasserin aut Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2007 Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. Grey model Artificial neural network Wastewater treatment plant Conventional activated sludge process Industrial park Chuang, S. H. aut Wan, T. J. aut Lo, H. M. aut Tsai, Y. P. aut Su, H. C. aut Yu, L. F. aut Hu, H. C. aut Sung, P. J. aut Enthalten in Environmental monitoring and assessment Springer Netherlands, 1981 146(2008), 1-3 vom: 15. Jan., Seite 51-66 (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:146 year:2008 number:1-3 day:15 month:01 pages:51-66 https://doi.org/10.1007/s10661-007-0059-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL GBV_ILN_22 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 146 2008 1-3 15 01 51-66 |
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10.1007/s10661-007-0059-7 doi (DE-627)OLC207373071X (DE-He213)s10661-007-0059-7-p DE-627 ger DE-627 rakwb eng 333.7 VZ Pai, T. Y. verfasserin aut Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2007 Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. Grey model Artificial neural network Wastewater treatment plant Conventional activated sludge process Industrial park Chuang, S. H. aut Wan, T. J. aut Lo, H. M. aut Tsai, Y. P. aut Su, H. C. aut Yu, L. F. aut Hu, H. C. aut Sung, P. J. aut Enthalten in Environmental monitoring and assessment Springer Netherlands, 1981 146(2008), 1-3 vom: 15. Jan., Seite 51-66 (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:146 year:2008 number:1-3 day:15 month:01 pages:51-66 https://doi.org/10.1007/s10661-007-0059-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL GBV_ILN_22 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 146 2008 1-3 15 01 51-66 |
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10.1007/s10661-007-0059-7 doi (DE-627)OLC207373071X (DE-He213)s10661-007-0059-7-p DE-627 ger DE-627 rakwb eng 333.7 VZ Pai, T. Y. verfasserin aut Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2007 Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. Grey model Artificial neural network Wastewater treatment plant Conventional activated sludge process Industrial park Chuang, S. H. aut Wan, T. J. aut Lo, H. M. aut Tsai, Y. P. aut Su, H. C. aut Yu, L. F. aut Hu, H. C. aut Sung, P. J. aut Enthalten in Environmental monitoring and assessment Springer Netherlands, 1981 146(2008), 1-3 vom: 15. Jan., Seite 51-66 (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:146 year:2008 number:1-3 day:15 month:01 pages:51-66 https://doi.org/10.1007/s10661-007-0059-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL GBV_ILN_22 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 AR 146 2008 1-3 15 01 51-66 |
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Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters |
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Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters |
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Pai, T. Y. |
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Environmental monitoring and assessment |
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Pai, T. Y. Chuang, S. H. Wan, T. J. Lo, H. M. Tsai, Y. P. Su, H. C. Yu, L. F. Hu, H. C. Sung, P. J. |
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10.1007/s10661-007-0059-7 |
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comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters |
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Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters |
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Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. © Springer Science+Business Media B.V. 2007 |
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Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. © Springer Science+Business Media B.V. 2007 |
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Abstract In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids ($ SS_{eff} $) and chemical oxygen demand ($ COD_{eff} $) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for $ SS_{eff} $ and $ COD_{eff} $ could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. © Springer Science+Business Media B.V. 2007 |
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Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters |
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