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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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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.
    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.
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    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.

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