Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design...
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Autor*in:	Xia Bai [verfasserIn] Juliang Jin [verfasserIn] Shaowei Ning [verfasserIn] Chengguo Wu [verfasserIn] Yuliang Zhou [verfasserIn] Libing Zhang [verfasserIn] Yi Cui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	hydrological variable frequency analysis cloud transformation algorithm empirical frequency Pearson type III frequency curve annual precipitation northern Anhui province

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 18, p 3586
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:18, p 3586

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13183586

Katalog-ID:	DOAJ047176474

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520			\|a Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.
650		4	\|a hydrological variable frequency analysis
650		4	\|a cloud transformation algorithm
650		4	\|a empirical frequency
650		4	\|a Pearson type III frequency curve
650		4	\|a annual precipitation
650		4	\|a northern Anhui province
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allfieldsSound	10.3390/rs13183586 doi (DE-627)DOAJ047176474 (DE-599)DOAJ4717321f93cb431bb4e4e40dd0e1e349 DE-627 ger DE-627 rakwb eng Xia Bai verfasserin aut Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis. hydrological variable frequency analysis cloud transformation algorithm empirical frequency Pearson type III frequency curve annual precipitation northern Anhui province Science Q Juliang Jin verfasserin aut Shaowei Ning verfasserin aut Chengguo Wu verfasserin aut Yuliang Zhou verfasserin aut Libing Zhang verfasserin aut Yi Cui verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 18, p 3586 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:18, p 3586 https://doi.org/10.3390/rs13183586 kostenfrei https://doaj.org/article/4717321f93cb431bb4e4e40dd0e1e349 kostenfrei https://www.mdpi.com/2072-4292/13/18/3586 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 18, p 3586
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source	In Remote Sensing 13(2021), 18, p 3586 volume:13 year:2021 number:18, p 3586
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authorswithroles_txt_mv	Xia Bai @@aut@@ Juliang Jin @@aut@@ Shaowei Ning @@aut@@ Chengguo Wu @@aut@@ Yuliang Zhou @@aut@@ Libing Zhang @@aut@@ Yi Cui @@aut@@
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author	Xia Bai
spellingShingle	Xia Bai misc hydrological variable frequency analysis misc cloud transformation algorithm misc empirical frequency misc Pearson type III frequency curve misc annual precipitation misc northern Anhui province misc Science misc Q Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis
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topic_title	Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis hydrological variable frequency analysis cloud transformation algorithm empirical frequency Pearson type III frequency curve annual precipitation northern Anhui province
topic	misc hydrological variable frequency analysis misc cloud transformation algorithm misc empirical frequency misc Pearson type III frequency curve misc annual precipitation misc northern Anhui province misc Science misc Q
topic_unstemmed	misc hydrological variable frequency analysis misc cloud transformation algorithm misc empirical frequency misc Pearson type III frequency curve misc annual precipitation misc northern Anhui province misc Science misc Q
topic_browse	misc hydrological variable frequency analysis misc cloud transformation algorithm misc empirical frequency misc Pearson type III frequency curve misc annual precipitation misc northern Anhui province misc Science misc Q
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title	Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis
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title_full	Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis
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author_browse	Xia Bai Juliang Jin Shaowei Ning Chengguo Wu Yuliang Zhou Libing Zhang Yi Cui
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title_sort	cloud transform algorithm based model for hydrological variable frequency analysis
title_auth	Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis
abstract	Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.
abstractGer	Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.
abstract_unstemmed	Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.
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title_short	Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis
url	https://doi.org/10.3390/rs13183586 https://doaj.org/article/4717321f93cb431bb4e4e40dd0e1e349 https://www.mdpi.com/2072-4292/13/18/3586 https://doaj.org/toc/2072-4292
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author2	Juliang Jin Shaowei Ning Chengguo Wu Yuliang Zhou Libing Zhang Yi Cui
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up_date	2024-07-04T00:19:39.957Z
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Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis

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