IDEAS home Printed from https://ideas.repec.org/a/adp/ijesnr/v16y2019i3p64-73.html
   My bibliography  Save this article

Evaluation of Zohreh River Water Quality, Impacted by Natural and Anthropogenic Pollution Sources, Using Multivariate Statistical Techniques

Author

Listed:
  • Maryam Ravanbakhsh
  • Yaser Tahmasebi Birgani

    (Department of Environmental Health Engineering, School of Health, Ahvaz Jundishapur University of Medical Sciences, Iran)

  • Yaser Tahmasebi Birgani

    (Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Iran)

  • Maryam Dastoorpoor

    (Department of Medical Virology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Iran)

  • Kambiz Ahmadi Angali

    (Department of Biostatistics, Ahvaz Jundishapur University of Medical Sciences, Iran)

Abstract

Discriminant analysis (DA) and principal component analysis (PCA) as a multivariate statistical technique, were applied for the evaluation of temporal and spatial variation and the interpretation of a large complex water quality data set of the Zohre river basin, monitoring 16 water quality parameters collected over 16 - 45 years (1966 - 2013), in four stations sites (1554 observations for each parameter). Principal component analysis, applied to the data sets of the four studied stations resulted in four, four, four and three latent factors explaining 78.985, 83.828, 77.648, and 77.68 % of the total variance in water quality data sets of Kheirabad, Poleflour, Chambostan and Dehmolla stations, respectively. The factors obtained from PCA analysis indicate that the parameters responsible for water quality variations are mainly related to TDS, EC, Cl, Na, SAR and %Na (natural sources included Gypsum and salt crystal are frequently grown in supratidal, intertidal zones of Zohreh delta and Chamshir faults I and II fault zone, and anthropogenic source caused by sugarcane culture withdrawals, discharging of domestic and industrial wastewaters) in all studied stations. DA provided an important data reduction as it uses only seven parameters (discharge, temperature, electrical conductivity, HCO3- , Cl, %Na and T-Hardness), affording more than 58.5% correct assignations in temporal analysis.

Suggested Citation

  • Maryam Ravanbakhsh & Yaser Tahmasebi Birgani & Yaser Tahmasebi Birgani & Maryam Dastoorpoor & Kambiz Ahmadi Angali, 2019. "Evaluation of Zohreh River Water Quality, Impacted by Natural and Anthropogenic Pollution Sources, Using Multivariate Statistical Techniques," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 16(3), pages 64-73, January.
    • Handle: RePEc:adp:ijesnr:v:16:y:2019:i:3:p:64-73
    DOI: 10.19080/IJESNR.2019.16.555936
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/ijesnr/pdf/IJESNR.MS.ID.555936.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/ijesnr/IJESNR.MS.ID.555936.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/IJESNR.2019.16.555936?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junguo, Hu & Guomo, Zhou & Xiaojun, Xu, 2013. "Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data," Ecological Modelling, Elsevier, vol. 266(C), pages 86-96.
    2. Minhao Zhang & Zhiyu Zhang & Xuan Wang & Zhenliang Liao & Lijin Wang, 2024. "The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 6103-6119, December.
    3. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    4. Shanshan Wang & Joe Wiart, 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    5. Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    6. West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
    7. Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    8. Areerachakul, Sirilak & Sophatsathit, Peraphon & Lursinsap, Chidchanok, 2013. "Integration of unsupervised and supervised neural networks to predict dissolved oxygen concentration in canals," Ecological Modelling, Elsevier, vol. 261, pages 1-7.
    9. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    10. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    11. Tanujit Chakraborty & Ashis Kumar Chakraborty & Zubia Mansoor, 2019. "A hybrid regression model for water quality prediction," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1167-1178, December.
    12. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    13. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    14. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    15. Yumin Wang & Weijian Ran & Lei Wu & Yifeng Wu, 2019. "Assessment of River Water Quality Based on an Improved Fuzzy Matter-Element Model," IJERPH, MDPI, vol. 16(15), pages 1-11, August.
    16. Tat Pham Van & Pham Nu Ngoc Han & Minh Phap Dao, 2017. "Modelling of Dissolved Oxygen in Thi Vai River Water Incorporating Artificial Neural Network and Multivariable Regression," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 7(1), pages 11-18, November.
    17. Mehdi Vafakhah & Saeid Khosrobeigi Bozchaloei, 2020. "Regional Analysis of Flow Duration Curves through Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 283-294, January.
    18. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    19. Dorel Paraschiv & Cristiana Tudor & Radu Petrariu, 2015. "The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area," Sustainability, MDPI, vol. 7(2), pages 1-12, January.
    20. Ruibin Zhang & Xin Qian & Wenting Zhu & Hailong Gao & Wei Hu & Jinhua Wang, 2014. "Simulation and Evaluation of Pollution Load Reduction Scenarios for Water Environmental Management: A Case Study of Inflow River of Taihu Lake, China," IJERPH, MDPI, vol. 11(9), pages 1-19, September.

    More about this item

    Keywords

    earth and environment journals; environment journals; open access environment journals; peer reviewed environmental journals; open access; juniper publishers; ournal of Environmental Sciences; juniper publishers journals ; juniper publishers reivew;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adp:ijesnr:v:16:y:2019:i:3:p:64-73. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robert Thomas (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.