Computationally efficient approach for identification of fuzzy dynamic groundwater sampling network

Kumari, Komal; Jain, Sheetal Kumar; Dhar, Anirban

doi:10.1007/s10661-019-7467-3

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…The model was coupled with an integer optimization model to maximize the coverage of the polluted area with monitoring wells while satisfying design constraints. Kumari et al (2019) develop two approaches where spatiotemporal concentration values were considered as fuzzy numbers, and a non-dominated sorting genetic algorithm was used to minimize the total spatiotemporal concentration-variance and the total error of estimated mass over sampling locations and times while maximizing the spatial coverage of the sampling network subject to budgetary constraints. Bode et al (2019) employ an evolutionary multi-objective optimization algorithm to minimize the installation and operation costs of the network while identifying all possible contamination sources and detecting contamination as early as possible.…”

Section: Introductionmentioning

confidence: 99%

Delineation of a PFOA Plume and Assessment of Data Gaps in its Conceptual Model Using PlumeSeeker™

Ozbek,

Voorhies,

Howard

et al. 2023

Groundwater

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An accurate conceptual site model (CSM) and plume‐delineation at contamination sites are pre‐requisites for successful remediation and for satisfying regulators and stakeholders. PlumeSeeker™ is well‐suited for assessing data gaps in CSMs by using available site data and for identifying the optimal number and locations of sampling locations to delineate contaminant plumes. It is an enhancement of a university research code for plume delineation using geostatistical and stochastic modeling integrated with the groundwater modeling software MODFLOW‐SURFACT™. PlumeSeeker™ increases the overall confidence in the location of the plume boundary through a variance‐reduction approach that selects existing‐ or new monitoring wells for sampling based on minimizing the uncertainty in plume boundary and on new field information. Applicable at sites with or without existing monitoring wells, PlumeSeeker™ is particularly powerful for optimally allocating project resources (labor, well installation, and laboratory costs) between existing wells and sampling at new locations. An application of PlumeSeeker™ at Lakehurst, the naval component of Joint Base McGuire‐Dix‐Lakehurst in New Jersey, demonstrates how the cost of delineating the migration pathway of a perfluorooctanoic acid (PFOA) plume can be minimized by requiring only 9 new sampling locations in addition to samples from 2 existing wells for achieving a 70% reduction in plume uncertainty. In addition, the use of available site data in three different scenarios identified CSM data‐gaps in the source area and in the interaction between Manapaqua Branch and groundwater, where the observed high concentration in this area could have resulted from a combination of groundwater migration and induced infiltration.

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Section: Introductionmentioning

confidence: 99%