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A GA Based Iterative Model for Identification of Unknown Groundwater Pollution Sources Considering Noisy Data

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Nature-Inspired Methods for Metaheuristics Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 16))

Abstract

Genetic Algorithms have been applied in solving various complex engineering optimization problems. This chapter presented the application of Genetic Algorithms in identifying unknown groundwater pollution sources of an aquifer. The unknown groundwater pollution sources can be identified by using the inverse optimization model. The inverse optimization model minimizes the difference between the simulated and observed concentration at the observation locations for obtaining the unknown pollution sources. However, the model cannot be setup unless and until the number of pollutions sources are not known. As such, an iterative based methodology is used to obtain the exact number of pollution sources along with their source strength. Further, it is not always possible to accurately measure the concentration data in the field. As such an analysis has been carried out to evaluate the model performance when noisy data is used for the prediction of the sources. The performance of the model is evaluated using an illustrative study area.

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Author information

Authors and Affiliations

  1. College of Food Technology, Central Agricultural University, Imphal, India

    Leichombam Sophia

  2. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajib Kumar Bhattacharjya

Authors
  1. Leichombam Sophia
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  2. Rajib Kumar Bhattacharjya
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Editor information

Editors and Affiliations

  1. Ecole Central de Nantes, Nantes cedex 3, France

    Fouad Bennis

  2. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajib Kumar Bhattacharjya

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Sophia, L., Bhattacharjya, R.K. (2020). A GA Based Iterative Model for Identification of Unknown Groundwater Pollution Sources Considering Noisy Data. In: Bennis, F., Bhattacharjya, R. (eds) Nature-Inspired Methods for Metaheuristics Optimization. Modeling and Optimization in Science and Technologies, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-26458-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-26458-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26457-4

  • Online ISBN: 978-3-030-26458-1

  • eBook Packages: EngineeringEngineering (R0)

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