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Cite this DOI

10.46243/jst.2020.v5.i4.pp268-275 · Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools

APA (7th edition)

Longman Publishers (2020). Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools. *Journal of Science & Technology*, 268–275. https://doi.org/10.46243/jst.2020.v5.i4.pp268-275

⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.

BibTeX

@article{anon2020analysis,
  title     = {{Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {jul},
  number    = {Volume 5},
  pages     = {268--275},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i4.pp268-275},
  url       = {https://doi.org/10.46243/jst.2020.v5.i4.pp268-275},
  language  = {en},
  abstract  = {: This Study Statistically analyzes the deteriorating water quality of the River Ganga. Statistical techniques such as Water Quality index (WQI), Cluster Analysis, Best Subsets Regression and Multiple Regression Analysis were applied to seven water quality parameters, collected from 21 sampling Stations in India. Water Quality Index identified the most polluted stations that are Kadaghat, Allahabad, Khurgi, Patna U/S, Bihar, Varanasi D/S (Malviya Bridge), U.P, Indrapuri, Dehri and Varanasi U/S (ASSIGHAT), U.P. Cluster Analysis for the different Stations showed a similarity of 99.99\% between the stations Ganga D/S, Mirzapur , Varanasi D/S (Malviya Bridge) and Varanasi U/S (Assighat), U.P. Cluster Analysis for variables showed a 98.96\% similarity of parameter BOD with WQI and 96.06\% similarity between the parameters Total Coliform and Fecal Coliform. After applied the Best Subset Regression Analysis we get the highest Mallow c-p value with high R2 for the parameters BOD, Nitrate, Total Coliform and Fecal Coliform. In the Regression analysis the p value for the estimated coefficients of BOD is 0.00, indicates that BOD is significantly related to WQI.In this paper we conclude that BOD is the most critical parameter and we study the comparison of water quality of river Ganga for different stations.}
}

⬇ .bib

RIS (EndNote, Zotero, Mendeley)

TY  - JOUR
TI  - Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/07/30/
IS  - Volume 5
SP  - 268
EP  - 275
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - : This Study Statistically analyzes the deteriorating water quality of the River Ganga. Statistical techniques such as Water Quality index (WQI), Cluster Analysis, Best Subsets Regression and Multiple Regression Analysis were applied to seven water quality parameters, collected from 21 sampling Stations in India. Water Quality Index identified the most polluted stations that are Kadaghat, Allahabad, Khurgi, Patna U/S, Bihar, Varanasi D/S (Malviya Bridge), U.P, Indrapuri, Dehri and Varanasi U/S (ASSIGHAT), U.P. Cluster Analysis for the different Stations showed a similarity of 99.99% between the stations Ganga D/S, Mirzapur , Varanasi D/S (Malviya Bridge) and Varanasi U/S (Assighat), U.P. Cluster Analysis for variables showed a 98.96% similarity of parameter BOD with WQI and 96.06% similarity between the parameters Total Coliform and Fecal Coliform. After applied the Best Subset Regression Analysis we get the highest Mallow c-p value with high R2 for the parameters BOD, Nitrate, Total Coliform and Fecal Coliform. In the Regression analysis the p value for the estimated coefficients of BOD is 0.00, indicates that BOD is significantly related to WQI.In this paper we conclude that BOD is the most critical parameter and we study the comparison of water quality of river Ganga for different stations.
DO  - 10.46243/jst.2020.v5.i4.pp268-275
UR  - https://doi.org/10.46243/jst.2020.v5.i4.pp268-275
ER  -

⬇ .ris

CSL-JSON

{
    "type": "article-journal",
    "id": "10.46243/jst.2020.v5.i4.pp268-275",
    "DOI": "10.46243/jst.2020.v5.i4.pp268-275",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i4.pp268-275",
    "title": "Analysis of Different Water Quality Parameters of Ganga River by Multivariate Tools",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "issued": {
        "date-parts": [
            [
                2020,
                7,
                30
            ]
        ]
    },
    "issue": "Volume 5",
    "page": "268-275",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": ": This Study Statistically analyzes the deteriorating water quality of the River Ganga. Statistical techniques such as Water Quality index (WQI), Cluster Analysis, Best Subsets Regression and Multiple Regression Analysis were applied to seven water quality parameters, collected from 21 sampling Stations in India. Water Quality Index identified the most polluted stations that are Kadaghat, Allahabad, Khurgi, Patna U/S, Bihar, Varanasi D/S (Malviya Bridge), U.P, Indrapuri, Dehri and Varanasi U/S (ASSIGHAT), U.P. Cluster Analysis for the different Stations showed a similarity of 99.99% between the stations Ganga D/S, Mirzapur , Varanasi D/S (Malviya Bridge) and Varanasi U/S (Assighat), U.P. Cluster Analysis for variables showed a 98.96% similarity of parameter BOD with WQI and 96.06% similarity between the parameters Total Coliform and Fecal Coliform. After applied the Best Subset Regression Analysis we get the highest Mallow c-p value with high R2 for the parameters BOD, Nitrate, Total Coliform and Fecal Coliform. In the Regression analysis the p value for the estimated coefficients of BOD is 0.00, indicates that BOD is significantly related to WQI.In this paper we conclude that BOD is the most critical parameter and we study the comparison of water quality of river Ganga for different stations.",
    "ISSN": "2456-5660"
}

⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.

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