Data from: ESTIMATION ON THE CONCENTRATION OF SUSPENDED SOLIDS FROM...
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EmailClick to copy linkLink copiedCiteAmanda R. M. de Oliveira; Alisson C. Borges; Antonio T. Matos; Moysés Nascimento (2023). ESTIMATION ON THE CONCENTRATION OF SUSPENDED SOLIDS FROM TURBIDITY IN THE WATER OF TWO SUB-BASINS IN THE DOCE RIVER BASIN [Dataset]. http://doi.org/10.6084/m9.figshare.7336739.v1jpegAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7336739.v1Dataset updatedMay 31, 2023AuthorsAmanda R. M. de Oliveira; Alisson C. Borges; Antonio T. Matos; Moysés NascimentoLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredDoce RiverDescriptionABSTRACT Knowing the relationship between the total suspended solids concentration (TSS), turbidity in the waters, and that turbidity analysis can be done faster and in less expensive way, this study aimed to obtain mathematical model to estimate the total suspended solids (TSS) concentration from the turbidity values for waters of Doce river basin. For this purpose, it was used the water quality database of the Minas Gerais Institute of Water Management (IGAM). The data were pre-treated using the adjusted boxplot technique followed by adjustment of curves for the different management units and rainfall regime period. It was verified the possibility of a single curve through the dummy variable technique, subsequently. With the results it was observed that the adjusted boxplot technique proved to be useful for environmental data. Linear relationships with R2 values, as a rule, were higher than 0.6, however, it was not possible to develop a single model. It is concluded that the generated models presented good adjustments being able to be used for predicting the concentration of TSS as a function of turbidity. However, each management unit in each period of rainfall regime presents particularities that were reflected in the prediction models.
Comparison of result on optimal design of industrial refrigeration system.
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xlsUpdated Jun 5, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Comparison of result on optimal design of industrial refrigeration system. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t015xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t015Dataset updatedJun 5, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionComparison of result on optimal design of industrial refrigeration system.
Comparison of result on welded beam design problem.
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xlsUpdated Jun 13, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Comparison of result on welded beam design problem. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t011xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t011Dataset updatedJun 13, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionComparison of result on welded beam design problem.
The comparison results of different algorithms on CEC2017 functions with...
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xlsUpdated Jun 6, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The comparison results of different algorithms on CEC2017 functions with D=30. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t004Dataset updatedJun 6, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe comparison results of different algorithms on CEC2017 functions with D=30.
The results of Wilcoxon rank sum test on CEC2019 functions.
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xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The results of Wilcoxon rank sum test on CEC2019 functions. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t009xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t009Dataset updatedJun 1, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe results of Wilcoxon rank sum test on CEC2019 functions.
Comparison of result on three-bar truss design problem.
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xlsUpdated Jun 13, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Comparison of result on three-bar truss design problem. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t013xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t013Dataset updatedJun 13, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionComparison of result on three-bar truss design problem.
The results of Wilcoxon rank sum test on CEC2017 functions with D=30.
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xlsUpdated Jun 13, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The results of Wilcoxon rank sum test on CEC2017 functions with D=30. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t008xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t008Dataset updatedJun 13, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe results of Wilcoxon rank sum test on CEC2017 functions with D=30.
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Description of CEC2017 benchmark functions.
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xlsUpdated Jun 13, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Description of CEC2017 benchmark functions. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t003xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t003Dataset updatedJun 13, 2023Dataset provided byPLOS ONEAuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionDescription of CEC2017 benchmark functions.
- f
The results of Wilcoxon rank sum test on 23 benchmark functions with D=30.
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xlsUpdated Jun 2, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The results of Wilcoxon rank sum test on 23 benchmark functions with D=30. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t007xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t007Dataset updatedJun 2, 2023Dataset provided byPLOS ONEAuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe results of Wilcoxon rank sum test on 23 benchmark functions with D=30.
The parameters of the algorithms.
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xlsUpdated Jun 4, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The parameters of the algorithms. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t001xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t001Dataset updatedJun 4, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe parameters of the algorithms.
The comparison results of different algorithms on 23 benchmark functions...
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xlsUpdated Jun 6, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The comparison results of different algorithms on 23 benchmark functions with D=30. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t002Dataset updatedJun 6, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe comparison results of different algorithms on 23 benchmark functions with D=30.
Comparison of result on speed reducer design problem.
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xlsUpdated Jun 2, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Comparison of result on speed reducer design problem. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t014xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t014Dataset updatedJun 2, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionComparison of result on speed reducer design problem.
The comparison results of different algorithms on CEC2019 functions.
- plos.figshare.com
xlsUpdated Jun 13, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). The comparison results of different algorithms on CEC2019 functions. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t006xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t006Dataset updatedJun 13, 2023AuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe comparison results of different algorithms on CEC2019 functions.
- f
Description of CEC2019 benchmark functions.
- plos.figshare.com
xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYu Li; Xiao Liang; Jingsen Liu; Huan Zhou (2023). Description of CEC2019 benchmark functions. [Dataset]. http://doi.org/10.1371/journal.pone.0276210.t005xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0276210.t005Dataset updatedJun 1, 2023Dataset provided byPLOS ONEAuthorsYu Li; Xiao Liang; Jingsen Liu; Huan ZhouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionDescription of CEC2019 benchmark functions.
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FacebookTwitterEmailData from: ESTIMATION ON THE CONCENTRATION OF SUSPENDED SOLIDS FROM TURBIDITY IN THE WATER OF TWO SUB-BASINS IN THE DOCE RIVER BASIN
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
ABSTRACT Knowing the relationship between the total suspended solids concentration (TSS), turbidity in the waters, and that turbidity analysis can be done faster and in less expensive way, this study aimed to obtain mathematical model to estimate the total suspended solids (TSS) concentration from the turbidity values for waters of Doce river basin. For this purpose, it was used the water quality database of the Minas Gerais Institute of Water Management (IGAM). The data were pre-treated using the adjusted boxplot technique followed by adjustment of curves for the different management units and rainfall regime period. It was verified the possibility of a single curve through the dummy variable technique, subsequently. With the results it was observed that the adjusted boxplot technique proved to be useful for environmental data. Linear relationships with R2 values, as a rule, were higher than 0.6, however, it was not possible to develop a single model. It is concluded that the generated models presented good adjustments being able to be used for predicting the concentration of TSS as a function of turbidity. However, each management unit in each period of rainfall regime presents particularities that were reflected in the prediction models.