Application of the Kapur entropy for two-dimensional velocity distribution

Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Singh, Gurpinder [verfasserIn] Khosa, Rakesh

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Velocity distribution Entropy parameter Shannon and Tsallis entropy Renyi and fractional entropy Kapur entropy Channel cross-section

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 37(2023), 9 vom: 13. Mai, Seite 3585-3598
Übergeordnetes Werk:	volume:37 ; year:2023 ; number:9 ; day:13 ; month:05 ; pages:3585-3598

Links:	Volltext

DOI / URN:	10.1007/s00477-023-02464-7

Katalog-ID:	SPR05293571X

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520			\|a Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error.
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allfields	10.1007/s00477-023-02464-7 doi (DE-627)SPR05293571X (SPR)s00477-023-02464-7-e DE-627 ger DE-627 rakwb eng Singh, Gurpinder verfasserin (orcid)0000-0001-8290-6903 aut Application of the Kapur entropy for two-dimensional velocity distribution 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213 Khosa, Rakesh aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2023), 9 vom: 13. Mai, Seite 3585-3598 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598 https://dx.doi.org/10.1007/s00477-023-02464-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2023 9 13 05 3585-3598
spelling	10.1007/s00477-023-02464-7 doi (DE-627)SPR05293571X (SPR)s00477-023-02464-7-e DE-627 ger DE-627 rakwb eng Singh, Gurpinder verfasserin (orcid)0000-0001-8290-6903 aut Application of the Kapur entropy for two-dimensional velocity distribution 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213 Khosa, Rakesh aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2023), 9 vom: 13. Mai, Seite 3585-3598 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598 https://dx.doi.org/10.1007/s00477-023-02464-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2023 9 13 05 3585-3598
allfields_unstemmed	10.1007/s00477-023-02464-7 doi (DE-627)SPR05293571X (SPR)s00477-023-02464-7-e DE-627 ger DE-627 rakwb eng Singh, Gurpinder verfasserin (orcid)0000-0001-8290-6903 aut Application of the Kapur entropy for two-dimensional velocity distribution 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213 Khosa, Rakesh aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2023), 9 vom: 13. Mai, Seite 3585-3598 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598 https://dx.doi.org/10.1007/s00477-023-02464-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2023 9 13 05 3585-3598
allfieldsGer	10.1007/s00477-023-02464-7 doi (DE-627)SPR05293571X (SPR)s00477-023-02464-7-e DE-627 ger DE-627 rakwb eng Singh, Gurpinder verfasserin (orcid)0000-0001-8290-6903 aut Application of the Kapur entropy for two-dimensional velocity distribution 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213 Khosa, Rakesh aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2023), 9 vom: 13. Mai, Seite 3585-3598 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598 https://dx.doi.org/10.1007/s00477-023-02464-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2023 9 13 05 3585-3598
allfieldsSound	10.1007/s00477-023-02464-7 doi (DE-627)SPR05293571X (SPR)s00477-023-02464-7-e DE-627 ger DE-627 rakwb eng Singh, Gurpinder verfasserin (orcid)0000-0001-8290-6903 aut Application of the Kapur entropy for two-dimensional velocity distribution 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213 Khosa, Rakesh aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2023), 9 vom: 13. Mai, Seite 3585-3598 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598 https://dx.doi.org/10.1007/s00477-023-02464-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2023 9 13 05 3585-3598
language	English
source	Enthalten in Stochastic environmental research and risk assessment 37(2023), 9 vom: 13. Mai, Seite 3585-3598 volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598
sourceStr	Enthalten in Stochastic environmental research and risk assessment 37(2023), 9 vom: 13. Mai, Seite 3585-3598 volume:37 year:2023 number:9 day:13 month:05 pages:3585-3598
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Velocity distribution Entropy parameter Shannon and Tsallis entropy Renyi and fractional entropy Kapur entropy Channel cross-section
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Singh, Gurpinder @@aut@@ Khosa, Rakesh @@aut@@
publishDateDaySort_date	2023-05-13T00:00:00Z
hierarchy_top_id	27160235X
id	SPR05293571X
language_de	englisch
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author	Singh, Gurpinder
spellingShingle	Singh, Gurpinder misc Velocity distribution misc Entropy parameter misc Shannon and Tsallis entropy misc Renyi and fractional entropy misc Kapur entropy misc Channel cross-section Application of the Kapur entropy for two-dimensional velocity distribution
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topic_title	Application of the Kapur entropy for two-dimensional velocity distribution Velocity distribution (dpeaa)DE-He213 Entropy parameter (dpeaa)DE-He213 Shannon and Tsallis entropy (dpeaa)DE-He213 Renyi and fractional entropy (dpeaa)DE-He213 Kapur entropy (dpeaa)DE-He213 Channel cross-section (dpeaa)DE-He213
topic	misc Velocity distribution misc Entropy parameter misc Shannon and Tsallis entropy misc Renyi and fractional entropy misc Kapur entropy misc Channel cross-section
topic_unstemmed	misc Velocity distribution misc Entropy parameter misc Shannon and Tsallis entropy misc Renyi and fractional entropy misc Kapur entropy misc Channel cross-section
topic_browse	misc Velocity distribution misc Entropy parameter misc Shannon and Tsallis entropy misc Renyi and fractional entropy misc Kapur entropy misc Channel cross-section
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title	Application of the Kapur entropy for two-dimensional velocity distribution
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title_full	Application of the Kapur entropy for two-dimensional velocity distribution
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title_sort	application of the kapur entropy for two-dimensional velocity distribution
title_auth	Application of the Kapur entropy for two-dimensional velocity distribution
abstract	Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract The present study uses the Kapur entropy to derive the two-dimensional velocity distribution applicable to the entire flow depth for wide and narrow open channel flows. Using the Maximum Entropy Principle and the predefined constraints, the Kapur entropy was maximized to derive the new velocity model under the assumption of velocity as a random variable. Moreover, the whole flow depth considered as a single region is predicted precisely by the derived velocity model, as evident from the validation performed using experimental and field data. The comparative analysis of the present model with four existing entropy-based models was done using a vast array of published velocity data. The new model has demonstrated excellent concordance with observed data, and its prediction accuracy is also checked through the statistical analysis of the absolute error. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	9
title_short	Application of the Kapur entropy for two-dimensional velocity distribution
url	https://dx.doi.org/10.1007/s00477-023-02464-7
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author2	Khosa, Rakesh
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doi_str	10.1007/s00477-023-02464-7
up_date	2024-07-03T15:52:22.140Z
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Application of the Kapur entropy for two-dimensional velocity distribution

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