IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0251510.html
   My bibliography  Save this article

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Author

Listed:
  • Naser Shiri
  • Jalal Shiri
  • Zaher Mundher Yaseen
  • Sungwon Kim
  • Il-Moon Chung
  • Vahid Nourani
  • Mohammad Zounemat-Kermani

Abstract

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Suggested Citation

  • Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
    • Handle: RePEc:plo:pone00:0251510
    DOI: 10.1371/journal.pone.0251510
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251510
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0251510&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0251510?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. Baizhong Yan & Furong Yu & Xiao Xiao & Xinzhou Wang, 2019. "Groundwater quality evaluation using a classification model: a case study of Jilin City, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 735-751, November.
    2. Maroufpoor, Saman & Shiri, Jalal & Maroufpoor, Eisa, 2019. "Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables," Agricultural Water Management, Elsevier, vol. 215(C), pages 63-73.
    3. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    4. Babak Farjad & Majeed Pooyandeh & Anil Gupta & Mohammad Motamedi & Danielle Marceau, 2017. "Modelling Interactions between Land Use, Climate, and Hydrology along with Stakeholders’ Negotiation for Water Resources Management," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    5. Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.
    6. Mohamad Sakizadeh & Hassan Rahmatinia, 2017. "Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 8(4), pages 37-53, October.
    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 Falah Allawi & Sinan Q. Salih & Murizah Kassim & Majeed Mattar Ramal & Abdulrahman S. Mohammed & Zaher Mundher Yaseen, 2022. "Application of Computational Model Based Probabilistic Neural Network for Surface Water Quality Prediction," Mathematics, MDPI, vol. 10(21), pages 1-18, October.
    2. Mohammed Benaafi & Mohamed A. Yassin & A. G. Usman & S. I. Abba, 2022. "Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques," Sustainability, MDPI, vol. 14(4), pages 1-21, February.

    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. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Ali Barzkar & Mohammad Najafzadeh & Farshad Homaei, 2022. "Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1931-1952, February.
    3. Chun-Wei Chen, 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    4. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    5. Kisi, Ozgur & Khosravinia, Payam & Heddam, Salim & Karimi, Bakhtiar & Karimi, Nazir, 2021. "Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algor," Agricultural Water Management, Elsevier, vol. 256(C).
    6. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    7. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    8. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    9. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    10. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    11. Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    12. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    13. Neshat, Mehdi & Nezhad, Meysam Majidi & Mirjalili, Seyedali & Garcia, Davide Astiaso & Dahlquist, Erik & Gandomi, Amir H., 2023. "Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy," Energy, Elsevier, vol. 278(C).
    14. Rashid Amin & Muzammal Majeed & Farrukh Shoukat Ali & Adeel Ahmed & Mudassar Hussain, 2022. "Reliability Awareness Multiple Path Installation in Software Defined Networking using Machine Learning Algorithm," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 158-172, July.
    15. Maisa’a W. Shammout & Muhammad Shatanawi & Jim Nelson, 2018. "Curve Number Applications for Restoration the Zarqa River Basin," Sustainability, MDPI, vol. 10(3), pages 1-11, February.
    16. Meysam Alizamir & Ozgur Kisi & Ali Najah Ahmed & Cihan Mert & Chow Ming Fai & Sungwon Kim & Nam Won Kim & Ahmed El-Shafie, 2020. "Advanced machine learning model for better prediction accuracy of soil temperature at different depths," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-25, April.
    17. Samad Emamgholizadeh & Amin Seyedzadeh & Hadi Sanikhani & Eisa Maroufpoor & Gholamhosein Karami, 2022. "Numerical and artificial intelligence models for predicting the water advance in border irrigation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 558-575, January.
    18. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    19. Alawi, Omer A. & Kamar, Haslinda Mohamed & Homod, Raad Z. & Yaseen, Zaher Mundher, 2024. "Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors," Renewable Energy, Elsevier, vol. 225(C).
    20. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).

    More about this item

    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:plo:pone00:0251510. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.