A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran
Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rahbar, Akram [verfasserIn] Vadiati, Meysam [verfasserIn] Talkhabi, Mahdi [verfasserIn] Nadiri, Ata Allah [verfasserIn] Nakhaei, Mohammad [verfasserIn] Rahimian, Mahdi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 79(2020), 13 vom: 01. Juli |
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| Übergeordnetes Werk: |
volume:79 ; year:2020 ; number:13 ; day:01 ; month:07 |
| Links: |
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| DOI / URN: |
10.1007/s12665-020-09064-6 |
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SPR04019518X |
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| 520 | |a Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. | ||
| 650 | 4 | |a Arak plain |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cluster analysis |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Fuzzy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a -means clustering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Groundwater statistics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hydrogeochemical facies |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hydrogeochemical modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Iran |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Vadiati, Meysam |e verfasserin |4 aut | |
| 700 | 1 | |a Talkhabi, Mahdi |e verfasserin |4 aut | |
| 700 | 1 | |a Nadiri, Ata Allah |e verfasserin |4 aut | |
| 700 | 1 | |a Nakhaei, Mohammad |e verfasserin |4 aut | |
| 700 | 1 | |a Rahimian, Mahdi |e verfasserin |4 aut | |
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10.1007/s12665-020-09064-6 doi (DE-627)SPR04019518X (SPR)s12665-020-09064-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Rahbar, Akram verfasserin aut A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 Vadiati, Meysam verfasserin aut Talkhabi, Mahdi verfasserin aut Nadiri, Ata Allah verfasserin aut Nakhaei, Mohammad verfasserin aut Rahimian, Mahdi verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 79(2020), 13 vom: 01. Juli (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:79 year:2020 number:13 day:01 month:07 https://dx.doi.org/10.1007/s12665-020-09064-6 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_65 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_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_2008 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_2360 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_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 38.95 ASE AR 79 2020 13 01 07 |
| spelling |
10.1007/s12665-020-09064-6 doi (DE-627)SPR04019518X (SPR)s12665-020-09064-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Rahbar, Akram verfasserin aut A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 Vadiati, Meysam verfasserin aut Talkhabi, Mahdi verfasserin aut Nadiri, Ata Allah verfasserin aut Nakhaei, Mohammad verfasserin aut Rahimian, Mahdi verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 79(2020), 13 vom: 01. Juli (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:79 year:2020 number:13 day:01 month:07 https://dx.doi.org/10.1007/s12665-020-09064-6 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_65 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_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_2008 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_2360 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_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 38.95 ASE AR 79 2020 13 01 07 |
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10.1007/s12665-020-09064-6 doi (DE-627)SPR04019518X (SPR)s12665-020-09064-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Rahbar, Akram verfasserin aut A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 Vadiati, Meysam verfasserin aut Talkhabi, Mahdi verfasserin aut Nadiri, Ata Allah verfasserin aut Nakhaei, Mohammad verfasserin aut Rahimian, Mahdi verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 79(2020), 13 vom: 01. Juli (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:79 year:2020 number:13 day:01 month:07 https://dx.doi.org/10.1007/s12665-020-09064-6 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_65 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_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_2008 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_2360 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_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 38.95 ASE AR 79 2020 13 01 07 |
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10.1007/s12665-020-09064-6 doi (DE-627)SPR04019518X (SPR)s12665-020-09064-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Rahbar, Akram verfasserin aut A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 Vadiati, Meysam verfasserin aut Talkhabi, Mahdi verfasserin aut Nadiri, Ata Allah verfasserin aut Nakhaei, Mohammad verfasserin aut Rahimian, Mahdi verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 79(2020), 13 vom: 01. Juli (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:79 year:2020 number:13 day:01 month:07 https://dx.doi.org/10.1007/s12665-020-09064-6 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_65 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_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_2008 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_2360 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_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 38.95 ASE AR 79 2020 13 01 07 |
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10.1007/s12665-020-09064-6 doi (DE-627)SPR04019518X (SPR)s12665-020-09064-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Rahbar, Akram verfasserin aut A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 Vadiati, Meysam verfasserin aut Talkhabi, Mahdi verfasserin aut Nadiri, Ata Allah verfasserin aut Nakhaei, Mohammad verfasserin aut Rahimian, Mahdi verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 79(2020), 13 vom: 01. Juli (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:79 year:2020 number:13 day:01 month:07 https://dx.doi.org/10.1007/s12665-020-09064-6 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_65 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_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_2008 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_2360 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_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 38.95 ASE AR 79 2020 13 01 07 |
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Enthalten in Environmental earth sciences 79(2020), 13 vom: 01. Juli volume:79 year:2020 number:13 day:01 month:07 |
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Arak plain Cluster analysis Fuzzy logic Fuzzy -means clustering Groundwater statistics Hydrogeochemical facies Hydrogeochemical modeling Iran |
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Rahbar, Akram @@aut@@ Vadiati, Meysam @@aut@@ Talkhabi, Mahdi @@aut@@ Nadiri, Ata Allah @@aut@@ Nakhaei, Mohammad @@aut@@ Rahimian, Mahdi @@aut@@ |
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Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. 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Rahbar, Akram |
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Rahbar, Akram ddc 550 bkl 38.95 misc Arak plain misc Cluster analysis misc Fuzzy logic misc Fuzzy misc -means clustering misc Groundwater statistics misc Hydrogeochemical facies misc Hydrogeochemical modeling misc Iran A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran |
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550 ASE 38.95 bkl A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran Arak plain (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Fuzzy (dpeaa)DE-He213 -means clustering (dpeaa)DE-He213 Groundwater statistics (dpeaa)DE-He213 Hydrogeochemical facies (dpeaa)DE-He213 Hydrogeochemical modeling (dpeaa)DE-He213 Iran (dpeaa)DE-He213 |
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ddc 550 bkl 38.95 misc Arak plain misc Cluster analysis misc Fuzzy logic misc Fuzzy misc -means clustering misc Groundwater statistics misc Hydrogeochemical facies misc Hydrogeochemical modeling misc Iran |
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A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran |
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Rahbar, Akram Vadiati, Meysam Talkhabi, Mahdi Nadiri, Ata Allah Nakhaei, Mohammad Rahimian, Mahdi |
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hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy c-mean clustering methods in arak plain, iran |
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A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran |
| abstract |
Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. |
| abstractGer |
Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. |
| abstract_unstemmed |
Abstract In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region. |
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A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran |
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| score |
7.398033 |