IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i12d10.1007_s11269-021-02940-1.html
   My bibliography  Save this article

A Novel Approach Using Hybrid Fuzzy Vertex Method-MATLAB Framework Based on GMS Model for Quantifying Predictive Uncertainty Associated with Groundwater Flow and Transport Models

Author

Listed:
  • Mona Nemati

    (Islamic Azad University)

  • Mahmoud Mohammad Rezapour Tabari

    (University of Mazandaran
    Isfahan University and Technology)

  • Seyed Abbas Hosseini

    (Islamic Azad University)

  • Saman Javadi

    (University of Tehran)

Abstract

Identification of the uncertain parameters, which affecting on the qualitative behavior of the aquifer, and determining their effect on the uncertainty of the simulated nitrate concentration (NC) is one of the major challenges in the qualitative monitoring of aquifers. In this study, in order to determine the quantitative amount of uncertainty related to the simulated nitrate, an approach based on a hybrid of Groundwater Modeling System (GMS) model and Fuzzy Vertex Method (FVM) method was developed using the developed code for the relationship between aquifer simulation model and MATLAB environment. In this model, hydraulic conductivity, NC in aquifer recharge sources, longitudinal dispersivity coefficient, and specific storage parameters were considered as uncertain parameters in the distributed simulation model of the Ardabil aquifer. In the proposed approach, first the quantitative and qualitative (QQ) model of the aquifer was prepared using the GMS model and calibrated. Then, using the FVM method and developed MATALB code, the uncertain values appropriate for each of the aquifer active cells were determined. The results obtained from the monthly NC uncertainty show that with increasing the level of uncertainty, the uncertainty of the simulated NC increases significantly. For example, can be mentioned a 14-fold increase in the number of cells with variation of NC less than 10% in the September month. Also, the lowest and highest variation in the deterministic amount of NC is related to the months of Nov. and Sep. with concentration variations equal to [− 8.5, 8.35] and [− 23.43, 19.8] mg/L, respectively. The findings of this study show that the application of at least 10% uncertainty in the deterministic values of the simulated NC is necessary to provide a suitable view for quality monitoring of aquifer. A quantitative amount of monthly uncertainty in areas with nitrate concentrations greater than 50 mg/L indicates that the amount of uncertainty in these areas is higher than areas with nitrate concentrations less than 50 mg/L. This leads to errors in the monitoring of contaminated areas to eliminate contamination and quality restoration. Also, centralization of uncertainty is mainly concentrated in the northeastern, western and southwestern parts of Ardabil plain and the severity of uncertainty in the mentioned areas increases with the intensification of uncertainty and continues to the central areas. Finally, it must be said that hydraulic conductivity and NC in aquifer recharge sources, respectively, play the most important role in creating uncertainty and is necessary to be considered in the NC simulation models.

Suggested Citation

  • Mona Nemati & Mahmoud Mohammad Rezapour Tabari & Seyed Abbas Hosseini & Saman Javadi, 2021. "A Novel Approach Using Hybrid Fuzzy Vertex Method-MATLAB Framework Based on GMS Model for Quantifying Predictive Uncertainty Associated with Groundwater Flow and Transport Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4189-4215, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02940-1
    DOI: 10.1007/s11269-021-02940-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02940-1
    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/s11269-021-02940-1?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. H. Delottier & A. Pryet & A. Dupuy, 2017. "Why Should Practitioners be Concerned about Predictive Uncertainty of Groundwater Management Models?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 61-73, January.
    2. Mohammad Ebrahim Banihabib & Mahmoud Mohammad Rezapour Tabari & Mohsen Mohammad Rezapour Tabari, 2019. "Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3673-3689, September.
    3. Maryam Sadat Kahe & Saman Javadi & Abbas Roozbahani & Kourosh Mohammadi, 2021. "Parametric uncertainty analysis on hydrodynamic coefficients in groundwater numerical models using Monte Carlo method and RPEM," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11583-11606, August.
    4. Divya Srivastava & Raj Singh, 2015. "Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4607-4627, October.
    5. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    6. M. Mohammad Rezapour Tabari, 2015. "Conjunctive Use Management under Uncertainty Conditions in Aquifer Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2967-2986, June.
    7. Khadije Norouzi Khatiri & Mohammad Hossein Niksokhan & Amin Sarang & Asghar Kamali, 2020. "Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3585-3608, September.
    8. Xiaoli Zhang & Haixia Wang & Anbang Peng & Wenchuan Wang & Baojian Li & Xudong Huang, 2020. "Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1479-1493, March.
    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. Mahmoud Mohammad Rezapour Tabari & Mahbobeh Abyar, 2022. "Development a Novel Integrated Distributed Multi-objective Simulation-optimization Model for Coastal Aquifers Management Using NSGA-II and GMS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 75-102, January.

    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. Srishti Gaur & Arnab Bandyopadhyay & Rajendra Singh, 2021. "From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1889-1911, April.
    2. Omid Seyedashraf & Andrea Bottacin-Busolin & Julien J. Harou, 2021. "Many-Objective Optimization of Sustainable Drainage Systems in Urban Areas with Different Surface Slopes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2449-2464, June.
    3. Karner, Katrin & Schmid, Erwin & Schneider, Uwe A. & Mitter, Hermine, 2021. "Computing stochastic Pareto frontiers between economic and environmental goals for a semi-arid agricultural production region in Austria," Ecological Economics, Elsevier, vol. 185(C).
    4. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    5. Ali Al-Maktoumi & Mohammad Mahdi Rajabi & Slim Zekri & Chefi Triki, 2021. "A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3447-3462, September.
    6. Dariane, A.B. & Ghasemi, M. & Karami, F. & Azaranfar, A. & Hatami, S., 2021. "Crop pattern optimization in a multi-reservoir system by combining many-objective and social choice methods," Agricultural Water Management, Elsevier, vol. 257(C).
    7. R. Roozbahani & B. Abbasi & S. Schreider & J. Iversen, 2021. "Dam Location-Allocation under Multiple Hydrological Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 993-1009, February.
    8. Jongsung Kim & Myungjin Lee & Heechan Han & Donghyun Kim & Yunghye Bae & Hung Soo Kim, 2022. "Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    9. Sarah Ben Amor & Fateh Belaid & Ramzi Benkraiem & Boumediene Ramdani & Khaled Guesmi, 2023. "Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda," Annals of Operations Research, Springer, vol. 325(2), pages 771-793, June.
    10. Mirzaie, Nargis & Banihabib, Mohammad Ebrahim & shahdany, S. Mehdy hashemy & Randhir, Timothy O., 2021. "Fuzzy particle swarm optimization for conjunctive use of groundwater and reclaimed wastewater under uncertainty," Agricultural Water Management, Elsevier, vol. 256(C).
    11. Mojtaba Kadkhodazadeh & Saeed Farzin, 2022. "Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3901-3927, August.
    12. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    13. Sarmad Dashti Latif & Ali Najah Ahmed & Edlic Sathiamurthy & Yuk Feng Huang & Ahmed El-Shafie, 2021. "Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia," 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. 109(1), pages 351-369, October.
    14. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    15. L. Guneshwor & T. I. Eldho & A. Vinod Kumar, 2018. "Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1517-1538, March.
    16. Mohammad Ebrahim Banihabib & Mahmoud Mohammad Rezapour Tabari & Mohsen Mohammad Rezapour Tabari, 2019. "Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3673-3689, 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:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02940-1. 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.