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Advances in Computer Communication and Computational Sciences pp 153–162Cite as

Characterization of Groundwater Contaminant Sources by Utilizing MARS Based Surrogate Model Linked to Optimization Model

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 759))

Abstract

Unknown groundwater contaminant source characterization is the first necessary step in the contamination remediation process. Although the remediation of a contaminated aquifer needs precise information of contaminant sources, usually only sparse and limited data are available. Therefore, often the process of remediation of contaminated groundwater is difficult and inefficient. This study utilizes Multivariate Adaptive Regression Splines (MARS) algorithm to develop an efficient Surrogate Models based Optimization (SMO) for source characterizing. Genetic Algorithm (GA) is also applied as the optimization algorithm in this methodology. This study addresses groundwater source characterizations with respect to the contaminant locations, magnitudes, and time release in a heterogeneous multilayered contaminated aquifer site. In this study, it is specified that only limited concentration measurement values are available. Also, the contaminant concentration data were collected a long time after the start of first potential contaminant source(s) activities. The hydraulic conductivity values are available at limited locations. The performance evaluation solution results of the developed MARS based SMO for source characterizing in a heterogeneous aquifer site with limited concentration measurement, parameter values, and under hydraulic conductivity uncertainties are shown to be satisfactory in terms of source characterization accuracy.

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Author information

Authors and Affiliations

  1. Discipline of Civil Engineering, College of Science and Engineering, James Cook University, Townsville, Australia

    Shahrbanoo Hazrati-Yadkoori & Bithin Datta

  2. CRC for Contamination Assessment and Remediation of the Environment, CRC CARE, ATC Building University of Newcastle, Callaghan, Australia

    Bithin Datta

Authors
  1. Shahrbanoo Hazrati-Yadkoori
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  2. Bithin Datta
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Corresponding author

Correspondence to Shahrbanoo Hazrati-Yadkoori .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Missouri, Columbia, Missouri, USA

    Sanjiv K. Bhatia

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  4. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

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Hazrati-Yadkoori, S., Datta, B. (2019). Characterization of Groundwater Contaminant Sources by Utilizing MARS Based Surrogate Model Linked to Optimization Model. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_14

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