Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China
This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-03-01
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| Series: | Water Supply |
| Subjects: | |
| Online Access: | http://ws.iwaponline.com/content/21/2/723 |
| _version_ | 1818428059402895360 |
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| author | Ruichen Xu Yong Pang Zhibing Hu |
| author_facet | Ruichen Xu Yong Pang Zhibing Hu |
| author_sort | Ruichen Xu |
| collection | DOAJ |
| description | This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments. HIGHLIGHTS
Introduce a MARS – machine learning method coupled with a Sobol sensitive analysis approach.;
Coupled methods can solve the same problems with less time.;
The declared goal of this research is to provide a certain scientific basis for future intelligent management of lake environments.; |
| first_indexed | 2024-12-14T14:55:36Z |
| format | Article |
| id | doaj.art-f01b512d83784b81be1acc784be1c47a |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-12-14T14:55:36Z |
| publishDate | 2021-03-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-f01b512d83784b81be1acc784be1c47a2022-12-21T22:57:00ZengIWA PublishingWater Supply1606-97491607-07982021-03-0121272373510.2166/ws.2020.359359Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, ChinaRuichen Xu0Yong Pang1Zhibing Hu2 Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, China and College of Environment, Hohai University, Nanjing 210098, China Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, China and College of Environment, Hohai University, Nanjing 210098, China Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, China and College of Environment, Hohai University, Nanjing 210098, China This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments. HIGHLIGHTS Introduce a MARS – machine learning method coupled with a Sobol sensitive analysis approach.; Coupled methods can solve the same problems with less time.; The declared goal of this research is to provide a certain scientific basis for future intelligent management of lake environments.;http://ws.iwaponline.com/content/21/2/723cluster analysismarssensitivity analysissoboltai lake |
| spellingShingle | Ruichen Xu Yong Pang Zhibing Hu Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China Water Supply cluster analysis mars sensitivity analysis sobol tai lake |
| title | Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China |
| title_full | Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China |
| title_fullStr | Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China |
| title_full_unstemmed | Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China |
| title_short | Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China |
| title_sort | sensitivity analysis of external conditions based on the mars sobol method case study of tai lake china |
| topic | cluster analysis mars sensitivity analysis sobol tai lake |
| url | http://ws.iwaponline.com/content/21/2/723 |
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