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

Leichombam, Sophia; Bhattacharjya, Rajib Kumar

doi:10.1007/978-3-030-26458-1_17

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Hence, an analysis is carried out to evaluate the performance of the proposed algorithm when a randomly generated measurement error is added to the simulated values. The following equation is employed to generate measurement error (Sophia and Bhattacharjya, 2020): Chang and Hanna, 2005;Moonen and Allegrini, 2015.…”

Section: Effect Of Noisy Datamentioning

confidence: 99%

Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Dodangeh

Rajabi

Carrera

et al. 2022

Journal of Contaminant Hydrology

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Section: Effect Of Noisy Datamentioning

confidence: 99%

Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Dodangeh

Rajabi

Carrera

et al. 2022

Journal of Contaminant Hydrology

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“…(Aral et al 2001). The GA optimization was combined with transport model by Ayaz (2017) and integrated with MODFLOW and MT3DMS models by Sophia and Bhattacharjya (2020) to simulate the groundwater contaminant transport.…”

Section: Introductionmentioning

confidence: 99%

Understanding the effects of subsidence on unconfined aquifer parameters by integration of Lattice Boltzmann Method (LBM) and Genetic Algorithm (GA)

Yousefi

Talebbeydokhti²,

Afzali³

et al. 2022

Nat Hazards

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Excessive exploitation of groundwater has led to a significant subsidence in Darab plain, located in Fars province, Iran. The compaction of aquifer layers especially in fine-grain sediments leads to changes in aquifer parameters including hydraulic conductivity, specific yield, and compressibility which consequently changes the permeability and storage of the aquifer. Accordingly, a precise estimation of aquifer parameters are essential for both future water resources planning and management. The main objective of this paper is to determine the most optimum values of aquifer parameters based on the groundwater information and subsidence measurements spanning from 2010 to 2016. The proposed approach for inverse solution of groundwater flow is based on Lattice Boltzmann Method (LBM) integrated with Genetic Algorithm for optimization (GA). Subsidence measurements made by previous studies as well as the initial values of unknown parameters obtained from piezometric information are incorporated into the inverse modeling. The whole process of inverse modeling is repeated from 2010 to 2016 which leads to annually estimation of the aquifer parameters. Due to the compaction occurring in the aquifer system, a decreasing temporal trend is observed in the aquifer parameters in most parts of the plain. By fitting a function to time-dependent aquifer parameters, their corresponding values and consequently the amount of subsidence in 2017 are predicted. The small average relative error (~3.5%) between the predicted land subsidence and the measurements demonstrates the high performance of the proposed inverse modeling approach.

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“…There are very few methods which simultaneously identify the locations and release flux histories of groundwater pollution sources [8][9]. One of the biggest limitations of these methods are that these methods assume the location of the sources to be anywhere in the study area, which makes these methods computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Groundwater Pollution Sources by Kriging Based Linked Simulation Optimization

Chakraborty¹,

Prakash²

2021

GEOMATE

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Often untreated wastes are buried underground as convenient method of disposal. These clandestine disposal methods are adopted to offset the cost incurred in proper waste management. Though there might be short term economic gain to the disposer but the long-term impact of such disposal manifests in form of groundwater pollution, which if left unchecked would potentially pollute the entire aquifer. Any contaminated aquifer treatment technique requires prior information about the pollutant sources characteristics. Thus, characterization of unknown groundwater pollution sources becomes imperative in remediation process. The first step involves the identification of number of sources and their locations. In a clandestine scenario the entire study area is treated as a potential source and the search for the actual source can be extensively rigorous in space and time. To overcome this challenge a kriging based zoning is adopted to narrow down the search space for the actual sources in a limited data availability scenario. A comparative analysis of two methods is presented; first method where only (Simulated Annealing based Linked Simulation Optimization) SALSO is used, and second method where kriging based zoning plus SALSO is adopted for pollution source characterization. It is found that kriging method clubbed with SALSO gives significantly better results compared to initial results. A boundary of Indian Institute of Technology Patna with hypothetical hydro-geological parameters is used to test the developed method. The location of the pollutant sources is easily identified when a kriging based Simulated Annealing Linked Simulation Optimization (KSALSO) method is adopted.

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

Cited by 6 publications

References 22 publications

Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Understanding the effects of subsidence on unconfined aquifer parameters by integration of Lattice Boltzmann Method (LBM) and Genetic Algorithm (GA)

Characterization of Groundwater Pollution Sources by Kriging Based Linked Simulation Optimization

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