IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i12d10.1007_s10668-022-02687-8.html
   My bibliography  Save this article

Indexical and artificial neural network modeling of the quality, corrosiveness, and encrustation potential of groundwater in industrialized metropolises, Southeast Nigeria

Author

Listed:
  • Johnbosco C. Egbueri

    (Chukwuemeka Odumegwu Ojukwu University)

  • Chinanu O. Unigwe

    (Alex Ekwueme Federal University)

  • Johnson C. Agbasi

    (Chukwuemeka Odumegwu Ojukwu University)

  • Vincent E. Nwazelibe

    (Albert Ludwig University of Freiburg)

Abstract

Adequate evaluation, monitoring, and prediction of groundwater resources are essential because humans heavily rely on groundwater for drinking, domestic, and industrial needs. The current study aimed at evaluating the quality of groundwater for drinking and industrial purposes in Awka and Nnewi urban metropolises (southeastern Nigeria) using indexical and artificial neural network (ANN) methods. The temperature of the studied groundwaters was found to range from 23 to 28 °C. The pH values revealed that the waters are acidic, though the groundwaters in Awka are acidic more than those in Nnewi. The majority of the analyzed physicochemical parameters (conductivity, total dissolved solids, Cl, SO4, HCO3, and Ca) examined were found to be below acceptable standard limits. In the metropolises, integrated water quality index (IWQI) classified over 75% of the groundwaters as unfit for drinking. Except for the Revelle index (RI), which classified 70% of the water samples within the Awka metropolis as slightly affected by salinization and 90% as strongly affected by salinization in the Nnewi metropolis, all other corrosivity and encrustation potential indices (Larson–Skold index (LSI), chloride–sulfate mass ration (CSMR), Langelier index (LI), aggressive index (AI), Ryznar stability index (RSI), and Puckorius (PSI)) utilized classified all the groundwater as having a high corrosivity. This demonstrates that the groundwaters in both metropolises have higher corrosion potential than encrustation potential. Additionally, the eight ANN models produced in this study function admirably. The ANN models performed well in the order IWQI > LSI > RSI > CSMR > LI > AI > PSI > RI according to R2 values. High performance of the models was validated by the R2, residual error, relative error, and sum of square error values. The findings of this paper would offer valuable insights for sustainable and strategic management of the groundwater resources.

Suggested Citation

  • Johnbosco C. Egbueri & Chinanu O. Unigwe & Johnson C. Agbasi & Vincent E. Nwazelibe, 2023. "Indexical and artificial neural network modeling of the quality, corrosiveness, and encrustation potential of groundwater in industrialized metropolises, Southeast Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(12), pages 14753-14783, December.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:12:d:10.1007_s10668-022-02687-8
    DOI: 10.1007/s10668-022-02687-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02687-8
    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/s10668-022-02687-8?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. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    2. Nima Tavanpour & Masoud Noshadi & Navid Tavanpour, 2016. "Scale Formation and Corrosion of Drinking Water Pipes: A Case Study of Drinking Water Distribution System of Shiraz City," Modern Applied Science, Canadian Center of Science and Education, vol. 10(3), pages 166-166, March.
    3. Alper Baba, 2015. "Application of geothermal energy and its environmental problems in Turkey," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 14(3/4), pages 321-331.
    4. Christian Agatemor & Patrick O. Okolo, 2008. "Studies of corrosion tendency of drinking water in the distribution system at the University of Benin," Environment Systems and Decisions, Springer, vol. 28(4), pages 379-384, December.
    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. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Jie Zhang & Meilian Liu & Qinfei Li, 2024. "Transboundary water pollution coordination decision-making model: an application in Taihu Basin in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(3), pages 5561-5578, March.
    3. Monika Kulisz & Justyna Kujawska & Bartosz Przysucha & Wojciech Cel, 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network," Energies, MDPI, vol. 14(18), pages 1-17, September.
    4. Wahid Ali Hamood Altowayti & Shafinaz Shahir & Taiseer Abdalla Elfadil Eisa & Maged Nasser & Muhammad Imran Babar & Abdullah Faisal Alshalif & Faris Ali Hamood AL-Towayti, 2022. "Smart Modelling of a Sustainable Biological Wastewater Treatment Technologies: A Critical Review," Sustainability, MDPI, vol. 14(22), pages 1-32, November.
    5. Johnbosco C. Egbueri, 2021. "Signatures of contamination, corrosivity and scaling in natural waters from a fast-developing suburb (Nigeria): insights into their suitability for industrial purposes," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 591-609, January.
    6. André Felipe Henriques Librantz & Fábio Cosme Rodrigues dos Santos, 2023. "Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    7. Yas Barzegar & Irina Gorelova & Francesco Bellini & Fabrizio D’Ascenzo, 2023. "Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)," IJERPH, MDPI, vol. 20(15), pages 1-20, August.
    8. 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.
    9. Paola Ortiz-Grisales & Julián Patiño-Murillo & Eduardo Duque-Grisales, 2021. "Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions," Sustainability, MDPI, vol. 13(13), pages 1-13, June.
    10. Li, Fanghua & Ou, Xiaoduo, 2025. "A scientometric examination on geothermal energy application research," Renewable Energy, Elsevier, vol. 238(C).
    11. Seoro Lee & Jonggun Kim & Gwanjae Lee & Jiyeong Hong & Joo Hyun Bae & Kyoung Jae Lim, 2021. "Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method," Sustainability, MDPI, vol. 13(18), pages 1-20, September.

    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:25:y:2023:i:12:d:10.1007_s10668-022-02687-8. 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.