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Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation

Author

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  • Partha Majumder

    (Indian Institute of Technology Bombay)

  • T.I. Eldho

    (Indian Institute of Technology Bombay)

Abstract

We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.

Suggested Citation

  • Partha Majumder & T.I. Eldho, 2020. "Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 763-783, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:2:d:10.1007_s11269-019-02472-9
    DOI: 10.1007/s11269-019-02472-9
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    References listed on IDEAS

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    1. Sina Sadeghfam & Yousef Hassanzadeh & Rahman Khatibi & Ata Allah Nadiri & Marjan Moazamnia, 2019. "Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1123-1145, February.
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    Cited by:

    1. 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.
    2. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    3. Richard T. Lyons & Richard C. Peralta & Partha Majumder, 2020. "Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima," IJERPH, MDPI, vol. 17(3), pages 1-10, January.
    4. Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.
    5. Fan, Qiuyan & Hajiyeva, Aytan Merdan, 2022. "Nexus between energy efficiency finance and renewable energy development: Empirical evidence from G-7 economies," Renewable Energy, Elsevier, vol. 195(C), pages 1077-1086.
    6. Yu Chen & Guodong Liu & Xiaohua Huang & Yuchuan Meng, 2022. "Groundwater Remediation Design Underpinned By Coupling Evolution Algorithm With Deep Belief Network Surrogate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2223-2239, May.
    7. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.

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