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...

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Autores principales:	Ruichen Xu, Yong Pang, Zhibing Hu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	cluster analysis mars sensitivity analysis sobol tai lake Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/f01b512d83784b81be1acc784be1c47a
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spelling	oai:doaj.org-article:f01b512d83784b81be1acc784be1c47a2021-11-06T07:09:31ZSensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China1606-97491607-079810.2166/ws.2020.359https://doaj.org/article/f01b512d83784b81be1acc784be1c47a2021-03-01T00:00:00Zhttp://ws.iwaponline.com/content/21/2/723https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798This 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.;Ruichen XuYong PangZhibing HuIWA Publishingarticlecluster analysismarssensitivity analysissoboltai lakeWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 2, Pp 723-735 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cluster analysis mars sensitivity analysis sobol tai lake Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	cluster analysis mars sensitivity analysis sobol tai lake Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Ruichen Xu Yong Pang Zhibing Hu Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China
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.;
format	article
author	Ruichen Xu Yong Pang Zhibing Hu
author_facet	Ruichen Xu Yong Pang Zhibing Hu
author_sort	Ruichen Xu
title	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_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_sort	sensitivity analysis of external conditions based on the mars-sobol method: case study of tai lake, china
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/f01b512d83784b81be1acc784be1c47a
work_keys_str_mv	AT ruichenxu sensitivityanalysisofexternalconditionsbasedonthemarssobolmethodcasestudyoftailakechina AT yongpang sensitivityanalysisofexternalconditionsbasedonthemarssobolmethodcasestudyoftailakechina AT zhibinghu sensitivityanalysisofexternalconditionsbasedonthemarssobolmethodcasestudyoftailakechina
_version_	1718443830215180288

Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China

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