Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain)
Groundwater resources play a vital role in the development and sustainability of a territory. Therefore, in areas with low and irregular precipitation and limited access to surface water resources, the management of groundwater resources, in terms of quantity and quality, is the inseparable part of...
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| Format: | Article |
| Language: | fas |
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Marvdasht Branch, Islamic Azad University
2019-02-01
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| Series: | مهندسی منابع آب |
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| Online Access: | https://wej.marvdasht.iau.ir/article_3328_d7cd25410bfa6910579832439be78a6d.pdf |
| _version_ | 1797359677558751232 |
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| author | b a Ehsan Kamali Maskooni mohsen armin |
| author_facet | b a Ehsan Kamali Maskooni mohsen armin |
| author_sort | b a |
| collection | DOAJ |
| description | Groundwater resources play a vital role in the development and sustainability of a territory. Therefore, in areas with low and irregular precipitation and limited access to surface water resources, the management of groundwater resources, in terms of quantity and quality, is the inseparable part of sustainable development. In the present study, principal components analysis (PCA) was used to assess the quality of groundwater resources of Behbahan Plain, Khuzestan province, Iran. In order to perform a groundwater quality analysis, 12 hydro-chemical variables were selected in order. Results of the KMO index and the Bartlett's test illustrate that this is reasonable to use the PCA on the dataset, which is constructed based on the selected variables, for characterization and analysis of groundwater quality in the study area. According to outcomes of the PCA, the first two axes (PC1 and PC2) captured 85.31% of the total variance in the dataset. The first PCA axis (PC1) explained 76.68% of the variation in the hydro-chemical data that was mainly affected by 10 variables (TH, SAR, K, Na, Mg, Ca, Cl, SO, TDS and EC). The second component (PC2) contributes 8.622% of the total variance in the dataset that indicated strong positive correlation with HCO3 and negative correlation with pH. Also, in this study, the relationship between spatial variation of each selected variable and land use were investigated. |
| first_indexed | 2024-03-08T15:27:11Z |
| format | Article |
| id | doaj.art-25e6a63d84d44abcb990affeb1d0224f |
| institution | Directory Open Access Journal |
| issn | 2008-6377 2423-7191 |
| language | fas |
| last_indexed | 2024-03-08T15:27:11Z |
| publishDate | 2019-02-01 |
| publisher | Marvdasht Branch, Islamic Azad University |
| record_format | Article |
| series | مهندسی منابع آب |
| spelling | doaj.art-25e6a63d84d44abcb990affeb1d0224f2024-01-10T08:10:39ZfasMarvdasht Branch, Islamic Azad Universityمهندسی منابع آب2008-63772423-71912019-02-01113957723328Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain)b a0Ehsan Kamali Maskooni1mohsen armin2دانشجوی دکتری آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگانYoung Researcher and Eleit Club, Jiroft Branch, Islamic Azad University, Iranassistant professor, College of Agriculture and Natural Resources, Yasuj University, yasuj, iranGroundwater resources play a vital role in the development and sustainability of a territory. Therefore, in areas with low and irregular precipitation and limited access to surface water resources, the management of groundwater resources, in terms of quantity and quality, is the inseparable part of sustainable development. In the present study, principal components analysis (PCA) was used to assess the quality of groundwater resources of Behbahan Plain, Khuzestan province, Iran. In order to perform a groundwater quality analysis, 12 hydro-chemical variables were selected in order. Results of the KMO index and the Bartlett's test illustrate that this is reasonable to use the PCA on the dataset, which is constructed based on the selected variables, for characterization and analysis of groundwater quality in the study area. According to outcomes of the PCA, the first two axes (PC1 and PC2) captured 85.31% of the total variance in the dataset. The first PCA axis (PC1) explained 76.68% of the variation in the hydro-chemical data that was mainly affected by 10 variables (TH, SAR, K, Na, Mg, Ca, Cl, SO, TDS and EC). The second component (PC2) contributes 8.622% of the total variance in the dataset that indicated strong positive correlation with HCO3 and negative correlation with pH. Also, in this study, the relationship between spatial variation of each selected variable and land use were investigated.https://wej.marvdasht.iau.ir/article_3328_d7cd25410bfa6910579832439be78a6d.pdfwater qualityprinciple componentskmo testbehbahan plainpca method |
| spellingShingle | b a Ehsan Kamali Maskooni mohsen armin Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) مهندسی منابع آب water quality principle components kmo test behbahan plain pca method |
| title | Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) |
| title_full | Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) |
| title_fullStr | Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) |
| title_full_unstemmed | Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) |
| title_short | Assessment of groundwater quality using multivariate analysis (Case study: Behbahan plain) |
| title_sort | assessment of groundwater quality using multivariate analysis case study behbahan plain |
| topic | water quality principle components kmo test behbahan plain pca method |
| url | https://wej.marvdasht.iau.ir/article_3328_d7cd25410bfa6910579832439be78a6d.pdf |
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