Evaluation of mine water quality based on the PCA–PSO–BP model

To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiaqi Wang [verfasserIn] Yanli Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model

Übergeordnetes Werk:	In: Journal of Water and Climate Change - IWA Publishing, 2021, 15(2024), 2, Seite 593-606
Übergeordnetes Werk:	volume:15 ; year:2024 ; number:2 ; pages:593-606

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.2166/wcc.2023.604

Katalog-ID:	DOAJ09564587X

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ09564587X
003	DE-627
005	20240413114930.0
007	cr uuu---uuuuu
008	240413s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.2166/wcc.2023.604 \|2 doi
035			\|a (DE-627)DOAJ09564587X
035			\|a (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TD1-1066
050		0	\|a GE1-350
100	0		\|a Jiaqi Wang \|e verfasserin \|4 aut
245	1	0	\|a Evaluation of mine water quality based on the PCA–PSO–BP model
264		1	\|c 2024
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;
650		4	\|a bp neural network
650		4	\|a mine water quality evaluation
650		4	\|a particle swarm optimization (pso)
650		4	\|a principal component analysis (pca)
650		4	\|a pso–bp model
653		0	\|a Environmental technology. Sanitary engineering
653		0	\|a Environmental sciences
700	0		\|a Yanli Huang \|e verfasserin \|4 aut
773	0	8	\|i In \|t Journal of Water and Climate Change \|d IWA Publishing, 2021 \|g 15(2024), 2, Seite 593-606 \|w (DE-627)62604703X \|w (DE-600)2552186-X \|x 24089354 \|7 nnns
773	1	8	\|g volume:15 \|g year:2024 \|g number:2 \|g pages:593-606
856	4	0	\|u https://doi.org/10.2166/wcc.2023.604 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed \|z kostenfrei
856	4	0	\|u http://jwcc.iwaponline.com/content/15/2/593 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2040-2244 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2408-9354 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_4046
951			\|a AR
952			\|d 15 \|j 2024 \|e 2 \|h 593-606

Indexfelder

author_variant	j w jw y h yh
matchkey_str	article:24089354:2024----::vlainfieaeqaiyaeot
hierarchy_sort_str	2024
callnumber-subject-code	TD
publishDate	2024
allfields	10.2166/wcc.2023.604 doi (DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Jiaqi Wang verfasserin aut Evaluation of mine water quality based on the PCA–PSO–BP model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences Yanli Huang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 15(2024), 2, Seite 593-606 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:15 year:2024 number:2 pages:593-606 https://doi.org/10.2166/wcc.2023.604 kostenfrei https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed kostenfrei http://jwcc.iwaponline.com/content/15/2/593 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 15 2024 2 593-606
spelling	10.2166/wcc.2023.604 doi (DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Jiaqi Wang verfasserin aut Evaluation of mine water quality based on the PCA–PSO–BP model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences Yanli Huang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 15(2024), 2, Seite 593-606 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:15 year:2024 number:2 pages:593-606 https://doi.org/10.2166/wcc.2023.604 kostenfrei https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed kostenfrei http://jwcc.iwaponline.com/content/15/2/593 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 15 2024 2 593-606
allfields_unstemmed	10.2166/wcc.2023.604 doi (DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Jiaqi Wang verfasserin aut Evaluation of mine water quality based on the PCA–PSO–BP model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences Yanli Huang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 15(2024), 2, Seite 593-606 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:15 year:2024 number:2 pages:593-606 https://doi.org/10.2166/wcc.2023.604 kostenfrei https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed kostenfrei http://jwcc.iwaponline.com/content/15/2/593 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 15 2024 2 593-606
allfieldsGer	10.2166/wcc.2023.604 doi (DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Jiaqi Wang verfasserin aut Evaluation of mine water quality based on the PCA–PSO–BP model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences Yanli Huang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 15(2024), 2, Seite 593-606 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:15 year:2024 number:2 pages:593-606 https://doi.org/10.2166/wcc.2023.604 kostenfrei https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed kostenfrei http://jwcc.iwaponline.com/content/15/2/593 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 15 2024 2 593-606
allfieldsSound	10.2166/wcc.2023.604 doi (DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Jiaqi Wang verfasserin aut Evaluation of mine water quality based on the PCA–PSO–BP model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences Yanli Huang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 15(2024), 2, Seite 593-606 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:15 year:2024 number:2 pages:593-606 https://doi.org/10.2166/wcc.2023.604 kostenfrei https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed kostenfrei http://jwcc.iwaponline.com/content/15/2/593 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 15 2024 2 593-606
language	English
source	In Journal of Water and Climate Change 15(2024), 2, Seite 593-606 volume:15 year:2024 number:2 pages:593-606
sourceStr	In Journal of Water and Climate Change 15(2024), 2, Seite 593-606 volume:15 year:2024 number:2 pages:593-606
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model Environmental technology. Sanitary engineering Environmental sciences
isfreeaccess_bool	true
container_title	Journal of Water and Climate Change
authorswithroles_txt_mv	Jiaqi Wang @@aut@@ Yanli Huang @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	62604703X
id	DOAJ09564587X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ09564587X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413114930.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2166/wcc.2023.604</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09564587X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD1-1066</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jiaqi Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluation of mine water quality based on the PCA–PSO–BP model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bp neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mine water quality evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">particle swarm optimization (pso)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">principal component analysis (pca)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pso–bp model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental technology. Sanitary engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yanli Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Water and Climate Change</subfield><subfield code="d">IWA Publishing, 2021</subfield><subfield code="g">15(2024), 2, Seite 593-606</subfield><subfield code="w">(DE-627)62604703X</subfield><subfield code="w">(DE-600)2552186-X</subfield><subfield code="x">24089354</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:593-606</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2166/wcc.2023.604</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://jwcc.iwaponline.com/content/15/2/593</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2040-2244</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2408-9354</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="h">593-606</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Jiaqi Wang
spellingShingle	Jiaqi Wang misc TD1-1066 misc GE1-350 misc bp neural network misc mine water quality evaluation misc particle swarm optimization (pso) misc principal component analysis (pca) misc pso–bp model misc Environmental technology. Sanitary engineering misc Environmental sciences Evaluation of mine water quality based on the PCA–PSO–BP model
authorStr	Jiaqi Wang
ppnlink_with_tag_str_mv	@@773@@(DE-627)62604703X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TD1-1066
illustrated	Not Illustrated
issn	24089354
topic_title	TD1-1066 GE1-350 Evaluation of mine water quality based on the PCA–PSO–BP model bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model
topic	misc TD1-1066 misc GE1-350 misc bp neural network misc mine water quality evaluation misc particle swarm optimization (pso) misc principal component analysis (pca) misc pso–bp model misc Environmental technology. Sanitary engineering misc Environmental sciences
topic_unstemmed	misc TD1-1066 misc GE1-350 misc bp neural network misc mine water quality evaluation misc particle swarm optimization (pso) misc principal component analysis (pca) misc pso–bp model misc Environmental technology. Sanitary engineering misc Environmental sciences
topic_browse	misc TD1-1066 misc GE1-350 misc bp neural network misc mine water quality evaluation misc particle swarm optimization (pso) misc principal component analysis (pca) misc pso–bp model misc Environmental technology. Sanitary engineering misc Environmental sciences
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of Water and Climate Change
hierarchy_parent_id	62604703X
hierarchy_top_title	Journal of Water and Climate Change
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)62604703X (DE-600)2552186-X
title	Evaluation of mine water quality based on the PCA–PSO–BP model
ctrlnum	(DE-627)DOAJ09564587X (DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed
title_full	Evaluation of mine water quality based on the PCA–PSO–BP model
author_sort	Jiaqi Wang
journal	Journal of Water and Climate Change
journalStr	Journal of Water and Climate Change
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2024
contenttype_str_mv	txt
container_start_page	593
author_browse	Jiaqi Wang Yanli Huang
container_volume	15
class	TD1-1066 GE1-350
format_se	Elektronische Aufsätze
author-letter	Jiaqi Wang
doi_str_mv	10.2166/wcc.2023.604
author2-role	verfasserin
title_sort	evaluation of mine water quality based on the pca–pso–bp model
callnumber	TD1-1066
title_auth	Evaluation of mine water quality based on the PCA–PSO–BP model
abstract	To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;
abstractGer	To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;
abstract_unstemmed	To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046
container_issue	2
title_short	Evaluation of mine water quality based on the PCA–PSO–BP model
url	https://doi.org/10.2166/wcc.2023.604 https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed http://jwcc.iwaponline.com/content/15/2/593 https://doaj.org/toc/2040-2244 https://doaj.org/toc/2408-9354
remote_bool	true
author2	Yanli Huang
author2Str	Yanli Huang
ppnlink	62604703X
callnumber-subject	TD - Environmental Technology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.2166/wcc.2023.604
callnumber-a	TD1-1066
up_date	2024-07-03T15:45:14.814Z
_version_	1803573279922847744
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ09564587X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413114930.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2166/wcc.2023.604</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09564587X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJc56f48a54d9c441f8246dd98812bb2ed</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD1-1066</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jiaqi Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluation of mine water quality based on the PCA–PSO–BP model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bp neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mine water quality evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">particle swarm optimization (pso)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">principal component analysis (pca)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pso–bp model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental technology. Sanitary engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yanli Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Water and Climate Change</subfield><subfield code="d">IWA Publishing, 2021</subfield><subfield code="g">15(2024), 2, Seite 593-606</subfield><subfield code="w">(DE-627)62604703X</subfield><subfield code="w">(DE-600)2552186-X</subfield><subfield code="x">24089354</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:593-606</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2166/wcc.2023.604</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/c56f48a54d9c441f8246dd98812bb2ed</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://jwcc.iwaponline.com/content/15/2/593</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2040-2244</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2408-9354</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="h">593-606</subfield></datafield></record></collection>
score	7.400528

Nicht das Richtige dabei?

Schreiben Sie uns!

Evaluation of mine water quality based on the PCA–PSO–BP model

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?