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Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization

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  • Divya Srivastava
  • Raj Singh

Abstract

Contamination of groundwater poses serious threat to the human health and environment. It is difficult and expensive to clean up contaminated aquifers. Identification of unknown pollution sources is vital for adopting any remediation strategy. Groundwater flow and transport simulation model is used to generate necessary data for Artificial Neural Networks (ANN) model building processes. Breakthrough curves obtained for specified pollution scenario are characterized to reduce the inputs to ANN model. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics, flow parameters and transport parameters are outputs for ANN model. Identification of sources is performed with considerations of three cases—simultaneous estimation of unknown sources and flow parameter; simultaneous estimation of unknown sources, flow and transport parameters; and simultaneous estimation of unknown sources and boundary head. Characterization of uncertainty in source identification due to uncertainty in flow parameter, uncertainty in transport parameter and uncertainty in constant head boundary estimation is performed using fuzzy vertex alpha-cut techniques. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:13:p:4607-4627
    DOI: 10.1007/s11269-015-1078-8
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    References listed on IDEAS

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    1. Manish Jha & Bithin Datta, 2014. "Linked Simulation-Optimization based Dedicated Monitoring Network Design for Unknown Pollutant Source Identification using Dynamic Time Warping Distance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4161-4182, September.
    2. Raj Singh & Bithin Datta, 2007. "Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 557-572, March.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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