IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v23y2021i12d10.1007_s10668-021-01437-6.html
   My bibliography  Save this article

Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India

Author

Listed:
  • Saif Said

    (Aligarh Muslim University, (AMU))

  • Shadab Ali Khan

    (Aligarh Muslim University, (AMU))

Abstract

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as “very poor” status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as “not significant,” reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions.

Suggested Citation

  • Saif Said & Shadab Ali Khan, 2021. "Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18252-18277, December.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:12:d:10.1007_s10668-021-01437-6
    DOI: 10.1007/s10668-021-01437-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01437-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-021-01437-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Madhab Mondal & Lakshminarayan Satpati, 2020. "Human intervention on river system: a control system—a case study in Ichamati River, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5245-5271, August.
    2. Kuo, Jan-Tai & Hsieh, Ming-Han & Lung, Wu-Seng & She, Nian, 2007. "Using artificial neural network for reservoir eutrophication prediction," Ecological Modelling, Elsevier, vol. 200(1), pages 171-177.
    3. Ana Milanović Pešić & Jovana Brankov & Dragana Milijašević Joksimović, 2020. "Water quality assessment and populations’ perceptions in the National park Djerdap (Serbia): key factors affecting the environment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 2365-2383, March.
    4. Saif Said & Athar Hussain & Garima Sharma, 2018. "Water Quality Mapping of Yamuna River Stretch Passing Through Delhi State Using High Resolution Geoeye-2 Imagery," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 9(4), pages 23-35, October.
    5. Hazi Azamathulla & Aminuddin Ghani, 2011. "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1537-1544, April.
    6. Hong Yao & Tianhua Ni & Tongzhu Zhang, 2020. "Estimation of phosphorus flux into the sea through one reversing river using continuous turbidities and water quality modeling," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4251-4265, June.
    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. Ekaterini Hadjisolomou & Konstantinos Stefanidis & George Papatheodorou & Evanthia Papastergiadou, 2016. "Assessing the Contribution of the Environmental Parameters to Eutrophication with the Use of the “PaD” and “PaD2” Methods in a Hypereutrophic Lake," IJERPH, MDPI, vol. 13(8), pages 1-14, July.
    2. Xu, Yanhong & Peng, Hong & Yang, Yinqun & Zhang, Wanshun & Wang, Shuangling, 2014. "A cumulative eutrophication risk evaluation method based on a bioaccumulation model," Ecological Modelling, Elsevier, vol. 289(C), pages 77-85.
    3. Omid Bozorg-Haddad & Mohammad Delpasand & Sarvin ZamanZad-Ghavidel & Xuefeng Chu, 2024. "Developing a novel social–water capital index by gene expression programming," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 28187-28217, November.
    4. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    5. E. Fallah-Mehdipour & O. Bozorg Haddad & H. Orouji & M. Mariño, 2013. "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3261-3272, July.
    6. Bulent Tutmez & Mehmet Yuceer, 2013. "Regression Kriging Analysis for Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3307-3318, July.
    7. H. Azamathulla & Robert Jarrett, 2013. "Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 715-729, February.
    8. Mohamad Javad Alizadeh & Davoud Ahmadyar & Ali Afghantoloee, 2017. "Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1777-1794, April.
    9. Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    10. T. N. Tretiakova & Yu. A. Syromiatnikova & J. Brankov & M. M. Radovanović, 2021. "Nature Tourism in National Parks: the Visitors’ Perspective (a Case Study of the Zyuratkul NP, Russia)," Regional Research of Russia, Springer, vol. 11(4), pages 667-679, October.
    11. 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.
    12. Amir Hamzeh Haghiabi, 2017. "Modeling River Mixing Mechanism Using Data Driven Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 811-824, February.
    13. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    14. Patricia Jimeno-Sáez & Javier Senent-Aparicio & José M. Cecilia & Julio Pérez-Sánchez, 2020. "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    15. Mohammad Aghababaei & Amir Etemad-Shahidi & Ebrahim Jabbari & Milad Taghipour, 2017. "Estimation of Transverse Mixing Coefficient in Straight and Meandering Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3809-3827, September.
    16. 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.
    17. Yavuz Alkan, 2021. "User-focused alternative design model for Sarıçay channel and landscaping in terms of sports and health components: Çanakkale, Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7212-7229, May.
    18. J. Yazdi & A . Moridi, 2017. "Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2105-2125, May.
    19. E. Fallah-Mehdipour & O. Bozorg Haddad & M. Mariño, 2012. "Real-Time Operation of Reservoir System by Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4091-4103, November.
    20. Ana Milanović Pešić & Tamara Jojić Glavonjić & Stefan Denda & Dejana Jakovljević, 2023. "Sustainable Tourism Development and Ramsar Sites in Serbia: Exploring Residents’ Attitudes and Water Quality Assessment in the Vlasina Protected Area," Sustainability, MDPI, vol. 15(21), pages 1-27, October.

    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:spr:endesu:v:23:y:2021:i:12:d:10.1007_s10668-021-01437-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.