A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China
Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multic...
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| Autor*in: |
Wang, Yulin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Springer Berlin Heidelberg, 1994, 27(2020), 17 vom: 06. Apr., Seite 20934-20949 |
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| Übergeordnetes Werk: |
volume:27 ; year:2020 ; number:17 ; day:06 ; month:04 ; pages:20934-20949 |
| Links: |
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| DOI / URN: |
10.1007/s11356-020-08287-1 |
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OLC2040585273 |
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| 520 | |a Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. | ||
| 650 | 4 | |a Multicriteria uncertainty analysis | |
| 650 | 4 | |a Behavioral parameters | |
| 650 | 4 | |a Acceptable parameter values | |
| 650 | 4 | |a Chaohu Lake | |
| 650 | 4 | |a Eutrophication modeling | |
| 700 | 1 | |a Cheng, Haomiao |4 aut | |
| 700 | 1 | |a Wang, Liang |4 aut | |
| 700 | 1 | |a Hua, Zulin |4 aut | |
| 700 | 1 | |a He, Chengda |4 aut | |
| 700 | 1 | |a Cheng, Jilin |4 aut | |
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10.1007/s11356-020-08287-1 doi (DE-627)OLC2040585273 (DE-He213)s11356-020-08287-1-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Wang, Yulin verfasserin aut A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. Multicriteria uncertainty analysis Behavioral parameters Acceptable parameter values Chaohu Lake Eutrophication modeling Cheng, Haomiao aut Wang, Liang aut Hua, Zulin aut He, Chengda aut Cheng, Jilin aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 27(2020), 17 vom: 06. Apr., Seite 20934-20949 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:27 year:2020 number:17 day:06 month:04 pages:20934-20949 https://doi.org/10.1007/s11356-020-08287-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 27 2020 17 06 04 20934-20949 |
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10.1007/s11356-020-08287-1 doi (DE-627)OLC2040585273 (DE-He213)s11356-020-08287-1-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Wang, Yulin verfasserin aut A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. Multicriteria uncertainty analysis Behavioral parameters Acceptable parameter values Chaohu Lake Eutrophication modeling Cheng, Haomiao aut Wang, Liang aut Hua, Zulin aut He, Chengda aut Cheng, Jilin aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 27(2020), 17 vom: 06. Apr., Seite 20934-20949 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:27 year:2020 number:17 day:06 month:04 pages:20934-20949 https://doi.org/10.1007/s11356-020-08287-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 27 2020 17 06 04 20934-20949 |
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10.1007/s11356-020-08287-1 doi (DE-627)OLC2040585273 (DE-He213)s11356-020-08287-1-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Wang, Yulin verfasserin aut A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. Multicriteria uncertainty analysis Behavioral parameters Acceptable parameter values Chaohu Lake Eutrophication modeling Cheng, Haomiao aut Wang, Liang aut Hua, Zulin aut He, Chengda aut Cheng, Jilin aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 27(2020), 17 vom: 06. Apr., Seite 20934-20949 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:27 year:2020 number:17 day:06 month:04 pages:20934-20949 https://doi.org/10.1007/s11356-020-08287-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 27 2020 17 06 04 20934-20949 |
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10.1007/s11356-020-08287-1 doi (DE-627)OLC2040585273 (DE-He213)s11356-020-08287-1-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Wang, Yulin verfasserin aut A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. Multicriteria uncertainty analysis Behavioral parameters Acceptable parameter values Chaohu Lake Eutrophication modeling Cheng, Haomiao aut Wang, Liang aut Hua, Zulin aut He, Chengda aut Cheng, Jilin aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 27(2020), 17 vom: 06. Apr., Seite 20934-20949 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:27 year:2020 number:17 day:06 month:04 pages:20934-20949 https://doi.org/10.1007/s11356-020-08287-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 27 2020 17 06 04 20934-20949 |
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10.1007/s11356-020-08287-1 doi (DE-627)OLC2040585273 (DE-He213)s11356-020-08287-1-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Wang, Yulin verfasserin aut A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. Multicriteria uncertainty analysis Behavioral parameters Acceptable parameter values Chaohu Lake Eutrophication modeling Cheng, Haomiao aut Wang, Liang aut Hua, Zulin aut He, Chengda aut Cheng, Jilin aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 27(2020), 17 vom: 06. Apr., Seite 20934-20949 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:27 year:2020 number:17 day:06 month:04 pages:20934-20949 https://doi.org/10.1007/s11356-020-08287-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 27 2020 17 06 04 20934-20949 |
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a combination method for multicriteria uncertainty analysis and parameter estimation: a case study of chaohu lake in eastern china |
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A combination method for multicriteria uncertainty analysis and parameter estimation: a case study of Chaohu Lake in Eastern China |
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Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Abstract Eutrophication models are of great importance and are valuable tools for the development of policy and legislation. However, the parameter uncertainty and substantial computational cost lead to difficulties in decision-making, especially for complex models with multiple indicators. A multicriteria uncertainty analysis and parameter estimation (MUAPE) method, which selected behavioral parameters combined with Pareto domination and simultaneously obtained acceptable values for modeling by the maximum likelihood concept and kernel density estimation, was shown. This method, which did not assign thresholds and weights, was applied to analyze the uncertainty of the Chaohu Lake eutrophication model and estimate parameters. The results of the behavioral parameters were compared using different criterion sets, the relative error (RE) and the root mean square error (RMSE), and the results showed little discrepancy in terms of the effects on parameter uncertainty represented by the marginal probability density. The uncertainties of the parameters related to algal kinetics (i.e., BMR, PM, and KESS) were smaller than those of nutrient- and temperature-related parameters (i.e., KDN, Nitm, KTB, and KTHDR) for both sets of criteria. However, the reduction in the joint uncertainty of the two parameters was greater when RE was used than when RMSE was used. The acceptable values for the key parameters of the Chaohu Lake eutrophication model were also obtained by the RE criterion. The results strongly agreed with the observed values, and parameters could be applied for model prediction. This result indicated that the combination method was not only practical for reducing parameter uncertainty but also useful for determining parameter values. This method provides a basis for multicriteria uncertainty analysis and parameter estimation in eutrophication modeling. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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