Optimal monitoring network design and identification of unknown pollutant sources in polluted aquifers

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{37017-prakash-2014-thesis,
  author =       "Om Prakash",
  title =        "Optimal monitoring network design and identification
                 of unknown pollutant sources in polluted aquifers",
  school =       "School of Engineering and Physical Sciences, James
                 Cook University",
  year =         "2014",
  address =      "Australia",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://researchonline.jcu.edu.au/37017/",
  URL =          "http://researchonline.jcu.edu.au/37017/1/37017-prakash-2014-thesis.pdf",
  size =         "229 pages",
  abstract =     "Increasing stress from various anthropogenic
                 activities has resulted in widespread pollution of
                 groundwater resources. Often, when the pollutant is
                 first detected in groundwater, little is known about
                 the pollutant sources. Identification of source
                 characteristics in terms of locations, activity
                 initiation times, and source flux release histories and
                 activity durations are vital in planning effective
                 remediation measures and determining the liability of
                 the polluter. Groundwater pollution source
                 characterization is an inverse and ill-posed problem.
                 Finding a solution to this inverse problem remains a
                 challenging task due to uncertainties in accurately
                 predicting the aquifer response to source flux
                 injection, generally encountered sparsity of
                 concentration measurements in the field, and the
                 non-uniqueness in the aquifer response to the subjected
                 hydraulic and chemical stresses. This study presents
                 linked simulation-optimization, and sequential
                 monitoring network design based methodologies for
                 identification of unknown groundwater pollution source
                 characteristics.",
  absttract =    "Pollution in groundwater aquifers is generally first
                 detected in an arbitrarily located water supply well or
                 a group of wells. Often pollutants are detected much
                 after activity at the sources may have initiated, or
                 even after it has ceased to exist. There may be a gap
                 of years, or even decades, between the start of source
                 activity and detection of pollutants in an aquifer.
                 Other important issues in accurately identifying
                 unknown groundwater pollution source characteristics
                 are the quality, usability and extent of pollution
                 measurement data from the study area. Existing
                 methodologies for unknown groundwater pollution source
                 characterization have several limitations.
                 Methodologies developed in this study aim to address
                 some of these limitations. The major limitations
                 addressed in this study include:

                 i. sparsity of pollutant concentration measurement
                 data,

                 ii. inefficient monitoring network for concentration
                 measurements,

                 iii. difficulty in identifying the source
                 locations,

                 iv. difficulty in establishing the pollutant source
                 activity initiation time,

                 v. applicability of optimal source characterization
                 with missing observation data.",
  absttract =    "In many cases of aquifer pollution, especially in
                 clandestine underground disposal of toxic wastes, no
                 information is available about the number and location
                 of such sources. Moreover, monitoring wells where
                 pollution is first detected may not be optimally
                 located for accurately identifying the release history
                 of unknown pollution sources. A large number of
                 pollutant concentration measurements spread over time
                 and space is necessary for accurate source
                 identification. However, long term monitoring over a
                 large number of monitoring locations has budgetary
                 constraints. This study presents a sequential optimal
                 monitoring network design methodology based on
                 geostatistical kriging, a pollutant concentration
                 gradient based search for identification of source
                 locations, and a Genetic Programming (GP) based optimal
                 monitoring network design model for collecting
                 concentration measurements for efficient source
                 characterization.

                 To address the issue of unknown starting times of
                 activity of the sources, a new methodology is developed
                 for simultaneously identifying the starting times of
                 the activity of the sources and their flux release
                 history. A new optimum decision model is formulated and
                 solved such that the starting times of the activity of
                 the sources are directly obtained as solution.
                 Simulated Annealing (SA) is used for solving the
                 optimization problem with the starting time of
                 pollutant source activity incorporated as explicit
                 decision variable.

                 Subsequent to the detection of pollution in an aquifer,
                 a more formal methodology for source characterization
                 is generally initiated only after large numbers of
                 spatiotemporal concentration measurements, spaced over
                 a sufficiently long period of time, are obtained.
                 During this time, the spread of the pollutant continues
                 while temporal measurements are being obtained at
                 monitoring locations. A feedback-based sequential
                 methodology for efficient identification of unknown
                 pollutant source characteristics, integrating optimal
                 monitoring network design and an optimization based
                 source identification model, is developed. The main
                 advantage of this methodology is that source
                 characterization can start at the same time as when
                 pollutant is first detected in the aquifer. In every
                 sequence, feedback from the source identification model
                 improves the optimal monitoring network design and
                 vice-versa. This results in efficient and accurate
                 source characterization, within a few sequences of
                 source identification and monitoring network design.",
  absttract =    "The performances of the developed methodologies are
                 evaluated for different scenarios of groundwater
                 pollution incorporating transient flow and
                 advective-dispersive transport in heterogeneous
                 anisotropic conditions. The applicability of the
                 developed methodologies is tested for a real aquifer
                 site polluted with petrochemical waste (BTEX). These
                 evaluation results demonstrate the potential
                 applicability of the developed methodologies to
                 correctly estimate the unknown source flux's magnitude,
                 and location and source activity initiation times,
                 while improving the accuracy of source flux
                 identification. Results of performance evaluation of
                 each of these methodologies indicate their potential
                 for field application.",
  notes =        "Genetic Programming Models for Impact Factor
                 Assessment and Frequency Factor Assessment

                 Item ID: 37017 Supervisor Bithin Datta",
}

Genetic Programming entries for Om Prakash

Citations