Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems

Mahinthakumar, G.; Saad, Mohamed

doi:10.1061/(asce)0733-9496(2005)131:1(45)

Cited by 148 publications

(55 citation statements)

References 48 publications

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“…Thus, GA is used to identify the promising solution close to the global minimum which is used as the initial guess for SQP algorithm in order to converge to the global minimum (Katare et al, 2004, Wolf and Moros, 1997, Mahinthakumar and Sayeed, 2005.…”

Section: Parameters Estimationmentioning

confidence: 99%

Kinetic identification and experimental validation of continuous plug flow crystallisation

Zhao

Kamaraju

Hou³

et al. 2015

Chemical Engineering Science

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Section: Parameters Estimationmentioning

confidence: 99%

Kinetic identification and experimental validation of continuous plug flow crystallisation

Zhao

Kamaraju

Hou³

et al. 2015

Chemical Engineering Science

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“…Comprehensive review of source identification methodologies can be found in work by Atmadja and Bagtzoglou (2001b), Michalak and Kitanidis (2004), and Sun et al (2006a). Numerous works related to pollution source identification are available, like least square regression and linear programming with response matrix approach (Gorelick et al 1983), statistical pattern recognition (Datta et al 1989), random walk based backward tracking model (Bagtzoglou et al 1992), nonlinear maximum likelihood estimation (Wagner 1992), nonlinear optimization with embedding technique (Mahar and Datta 1997, 2001, correlation coefficient optimization (Sidauruk et al 1997), backward probabilistic model (Neupauer and Wilson 1999), geostatistical inversion approach (Snodgrass and Kitanidis 1997;Butera and Tanda 2003;Michalak and Kitanidis 2004), Tikhonov regularization (Skaggs and Kabala 1994;Liu and Ball 1999), quasi-reversibility (Skaggs and Kabala 1995;Bagtzoglou and Atmadja 2003), marching-jury backward beam equation (Atmadja and Bagtzoglou 2001a;Bagtzoglou and Atmadja 2003), genetic algorithm based approach (Aral et al 2001;Mahinthakumar and Sayeed 2005;Singh and Datta 2006), artificial neural network approach Datta 2004, 2007;, constrained robust least square approach (Sun et al 2006a, b), robust geostatistical approach (Sun 2007). However, only few studies have incorporated monitoring network within the source identification framework.…”

Section: Notationmentioning

confidence: 99%

Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

Datta

Chakrabarty

Dhar

2008

Water Resour Manage

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The identification of unknown pollution sources is a prerequisite for designing of a remediation strategy. In most of the real world situations, it is difficult to identify the pollution sources without a scientifically designed efficient monitoring network. The locations of the contaminant concentration measurement sites would determine the efficiency of the unknown source identification process to a large extent. Therefore coupled and iterative sequential source identification and dynamic monitoring network design framework is developed. The coupled approach provides a framework for necessary sequential exchange of information between monitoring network and source identification methodology. The preliminary identification of unknown sources, based on limited concentration data from existing arbitrarily located wells provides the initial rough estimate of the source fluxes. These identified source fluxes are then utilized for designing an optimal monitoring network for the first stage. Both the monitoring network and source identification process is repeated by sequential identification of sources and design of monitoring network which provides the feedback information. In the optimal source identification model, the Jacobian matrix which is the determinant for the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. For the optimal monitoring network design, the integer programming based optimal design model requires as input, simulated sets of concentration data. In the proposed methodology, the concentration measurement data from the designed and 2032 B. Datta et al.implemented monitoring network are used as feedback information for sequential identification of unknown pollution sources. The potential applicability of the developed methodology is demonstrated for an illustrative study area.

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“…Some of the important contributions include: reverse-time random particle method for finding the most probable source (Bagtzoglou et al 1992); inverse models based on nonlinear maximum-likelihood estimation to determine optimal estimates of the unknown model parameters and source characteristics (Wagner 1992); minimum relative entropy, a gradient-based optimization for solving source identification problems (Woodbury and Ulrych 1996;Woodbury et al 1998); embedded non-linear optimization technique for source identification (Mahar and Datta 1997; inverse procedures based on correlation coefficient optimization (Sidauruk et al 1997); Tikhonov regularization (Skaggs and Kabala 1994;Liu and Ball 1999); quasi-reversibility (Skaggs and Kabala 1995;Bagtzoglou and Atmadja 2003); marching-jury backward beam equation (Atmadja and Bagtzoglou 2001a;Bagtzoglou and Atmadja 2003;Bagtzoglou and Baun 2005); genetic algorithm (GA) based approach (Aral et al 2001;Mahinthakumar and Sayeed 2005;Singh and Datta 2006); artificial neural network (ANN) approach Datta 2004, 2007;); constrained robust least square approach (Sun et al 2006a, b); classical optimization based approach (Datta et al 2009a; probabilistic support vector machines (PSVMs) and probabilistic neural networks (PNNs) based probabilistic models ; inverse particle tracking approach (Bagtzoglou 2003;Ababou et al 2010); heuristic harmony search for source identification (Ayvaz 2010); simulated annealing (SA) as optimization for source identification (Jha and Datta 2011, 2012bPrakash and Datta 2014a). A review of different optimization techniques for solving source identification problem is presented in Chadalavada et al (2011) and Amirabdollahian and Datta (2013).…”

Section: Introductionmentioning

confidence: 99%

Optimal characterization of pollutant sources in contaminated aquifers by integrating sequential-monitoring-network design and source identification: methodology and an application in Australia

Prakash

Datta

2015

Hydrogeol J

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Often, when pollution is first detected in groundwater, very few spatiotemporal pollutant concentration measurements are available. The contaminant concentration measurement data initially available are generally sparse and insufficient for accurate source characterization. This requires development of a contaminant monitoring plan and its field implementation to collect more data. The location of scientifically chosen monitoring points and the number of measurements are important considerations in improving the source-characterization process, especially in a complex contamination scenario. In order to improve the efficiency of source characterization, a feedback-based methodology is implemented, integrating sequentialmonitoring-network design and a source identification method. The simulated annealing (SA) optimization algorithm is used to solve the models for optimal source identification and the monitoring-network-design optimization. This sequence is repeated a few times to improve the accuracy of source characterization. The methodology is based on the premise that concentration measurements from a sequence of implemented monitoring networks provide feedback information on the actual concentration in the site. This additional information, obtained as feedback from monitoring networks designed and implemented based on intermediate source characterization, can result in sequential improvement in the resulting source characterization. The performance of this methodology is evaluated by application to a contaminated aquifer site in New South Wales, Australia, where source location, sourceactivity initiation time and source-flux (mass per unit time) release history are considered as unknown variables. The performance evaluation results demonstrate potential applicability of the proposed sequential methodology.

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Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems

Cited by 148 publications

References 48 publications

Kinetic identification and experimental validation of continuous plug flow crystallisation

Kinetic identification and experimental validation of continuous plug flow crystallisation

Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

Optimal characterization of pollutant sources in contaminated aquifers by integrating sequential-monitoring-network design and source identification: methodology and an application in Australia

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