IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9687-d1172965.html
   My bibliography  Save this article

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Author

Listed:
  • Abdessamed Derdour

    (Artificial Intelligence Laboratory for Mechanical and Civil Structures and Soil, University Center of Naama, P.O. Box 66, Naama 45000, Algeria
    Laboratory for the Sustainable Management of Natural Resources in Arid and Semi-Arid Zones, University Center of Naama, P.O. Box 66, Naama 45000, Algeria)

  • Hazem Ghassan Abdo

    (Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria)

  • Hussein Almohamad

    (Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia)

  • Abdullah Alodah

    (Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia)

  • Ahmed Abdullah Al Dughairi

    (Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia)

  • Sherif S. M. Ghoneim

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Enas Ali

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

Abstract

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO 3 ), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

Suggested Citation

  • Abdessamed Derdour & Hazem Ghassan Abdo & Hussein Almohamad & Abdullah Alodah & Ahmed Abdullah Al Dughairi & Sherif S. M. Ghoneim & Enas Ali, 2023. "Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9687-:d:1172965
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9687/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9687/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Larry Mays, 2013. "Groundwater Resources Sustainability: Past, Present, and Future," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4409-4424, October.
    2. Esmaeil Asadi & Mohammad Isazadeh & Saeed Samadianfard & Mohammad Firuz Ramli & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Eva Hajnal & Kwok-Wing Chau, 2019. "Groundwater Quality Assessment for Sustainable Drinking and Irrigation," Sustainability, MDPI, vol. 12(1), pages 1-13, December.
    3. Insaf Babiker & Mohamed Mohamed & Tetsuya Hiyama, 2007. "Assessing groundwater quality using GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(4), pages 699-715, April.
    4. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    5. Muyen, Zahida & Moore, Graham A. & Wrigley, Roger J., 2011. "Soil salinity and sodicity effects of wastewater irrigation in South East Australia," Agricultural Water Management, Elsevier, vol. 99(1), pages 33-41.
    6. Oweis, Theib & Hachum, Ahmed, 2006. "Water harvesting and supplemental irrigation for improved water productivity of dry farming systems in West Asia and North Africa," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 57-73, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed Chakib Sekkal & Zakarya Ziani & Moustafa Yassine Mahdad & Sidi Mohammed Meliani & Mohammed Haris Baghli & Mohammed Zakaria Bessenouci, 2024. "Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance," Energies, MDPI, vol. 17(4), pages 1-34, February.
    2. Sani I. Abba & Mohamed A. Yassin & Auwalu Saleh Mubarak & Syed Muzzamil Hussain Shah & Jamilu Usman & Atheer Y. Oudah & Sujay Raghavendra Naganna & Isam H. Aljundi, 2023. "Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence," Sustainability, MDPI, vol. 15(21), pages 1-21, November.

    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. Samuel Sandoval-Solis & Jose Pablo Ortiz Partida & Lindsay Floyd, 2022. "Multi-Objective Water Planning in a Poor Water Data Region: Aragvi River Basin," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    2. Engin Baker & Secil Varbak Nese & Erkan Dursun, 2023. "Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis," Energies, MDPI, vol. 16(3), pages 1-11, January.
    3. Augustina Clara Alexander & Julius Ndambuki & Ramadhan Salim & Alex Manda, 2017. "Assessment of Spatial Variation of Groundwater Quality in a Mining Basin," Sustainability, MDPI, vol. 9(5), pages 1-14, May.
    4. Andersson, Jafet C.M. & Zehnder, Alexander J.B. & Rockström, Johan & Yang, Hong, 2011. "Potential impacts of water harvesting and ecological sanitation on crop yield, evaporation and river flow regimes in the Thukela River basin, South Africa," Agricultural Water Management, Elsevier, vol. 98(7), pages 1113-1124, May.
    5. Previati, M. & Bevilacqua, I. & Canone, D. & Ferraris, S. & Haverkamp, R., 2010. "Evaluation of soil water storage efficiency for rainfall harvesting on hillslope micro-basins built using time domain reflectometry measurements," Agricultural Water Management, Elsevier, vol. 97(3), pages 449-456, March.
    6. Hu, Yajin & Ma, Penghui & Zhang, Binbin & Hill, Robert L. & Wu, Shufang & Dong, Qin’ge & Chen, Guangjie, 2019. "Exploring optimal soil mulching for the wheat-maize cropping system in sub-humid drought-prone regions in China," Agricultural Water Management, Elsevier, vol. 219(C), pages 59-71.
    7. Wang, Wendi & Straffelini, Eugenio & Tarolli, Paolo, 2023. "Steep-slope viticulture: The effectiveness of micro-water storage in improving the resilience to weather extremes," Agricultural Water Management, Elsevier, vol. 286(C).
    8. B. Yan & X. Su & Y. Chen, 2009. "Functional Structure and Data Management of Urban Water Supply Network Based on GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2633-2653, October.
    9. Mabasa, Nyiko C. & Jones, Clifford L.W. & Laing, Mark, 2021. "The use of treated brewery effluent for salt tolerant crop irrigation," Agricultural Water Management, Elsevier, vol. 245(C).
    10. Maestre-Valero, J.F. & Gonzalez-Ortega, M.J. & Martinez-Alvarez, V. & Gallego-Elvira, B. & Conesa-Jodar, F.J. & Martin-Gorriz, B., 2019. "Revaluing the nutrition potential of reclaimed water for irrigation in southeastern Spain," Agricultural Water Management, Elsevier, vol. 218(C), pages 174-181.
    11. Xiaoqin Zhang & Hongbin Zhu & Bo Li & Ruihan Wu & Jun Jiang, 2022. "Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function," Energies, MDPI, vol. 15(12), pages 1-14, June.
    12. Robert L. Oxley & Larry W. Mays & Alan Murray, 2016. "Optimization Model for the Sustainable Water Resource Management of River Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3247-3264, July.
    13. Ali, Shahzad & Xu, Yueyue & Jia, Qianmin & Ahmad, Irshad & Ma, Xiangcheng & Yan, Zhang & Cai, Tie & Ren, Xiaolong & Zhang, Peng & Jia, Zhikuan, 2018. "Interactive effects of planting models with limited irrigation on soil water, temperature, respiration and winter wheat production under simulated rainfall conditions," Agricultural Water Management, Elsevier, vol. 204(C), pages 198-211.
    14. Zolfaghary, Parvin & Zakerinia, Mahdi & Kazemi, Hossein, 2021. "A model for the use of urban treated wastewater in agriculture using multiple criteria decision making (MCDM) and geographic information system (GIS)," Agricultural Water Management, Elsevier, vol. 243(C).
    15. Grum, Berhane & Hessel, Rudi & Kessler, Aad & Woldearegay, Kifle & Yazew, Eyasu & Ritsema, Coen & Geissen, Violette, 2016. "A decision support approach for the selection and implementation of water harvesting techniques in arid and semi-arid regions," Agricultural Water Management, Elsevier, vol. 173(C), pages 35-47.
    16. Rezaei, Ehsan Eyshi & Gaiser, Thomas, 2017. "Change in crop management strategies could double the maize yield in Africa," Discussion Papers 260154, University of Bonn, Center for Development Research (ZEF).
    17. Abbas Afshar & Mohamad Amin Tavakoli & Ali Khodagholi, 2020. "Multi-Objective Hydro-Economic Modeling for Sustainable Groundwater Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1855-1869, April.
    18. Miguel Pérez-Martín & Teodoro Estrela & Joaquín Andreu & Javier Ferrer, 2014. "Modeling Water Resources and River-Aquifer Interaction in the Júcar River Basin, Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4337-4358, September.
    19. Talebizadeh, Mansour & Moriasi, Daniel & Gowda, Prasanna & Steiner, Jean L. & Tadesse, Haile K. & Nelson, Amanda M. & Starks, Patrick, 2018. "Simultaneous calibration of evapotranspiration and crop yield in agronomic system modeling using the APEX model," Agricultural Water Management, Elsevier, vol. 208(C), pages 299-306.
    20. Zhi-Jun Li & Wei-Gen Chen & Jie Shan & Zhi-Yong Yang & Ling-Yan Cao, 2022. "Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis," Energies, MDPI, vol. 15(9), pages 1-22, April.

    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:gam:jsusta:v:15:y:2023:i:12:p:9687-:d:1172965. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.