Optimal Unknown Pollution Source Characterization in a Contaminated Groundwater Aquifer—Evaluation of a Developed Dedicated Software Tool

Datta, Bithin; Prakash, Om; Cassou, Pauline; Valetaud, Mathieu

doi:10.4236/gep.2014.25007

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Groundwater contamination occurs below the surface, it is difficult to detect and often has a lag time when it is detected. And little information was known about the sources of groundwater contamination when it was first discovered ( Datta, B et al, 2014;Snodgrass and Kitanidis, 1997). This poses great difficulties for the rational design of groundwater contamination remediation programs and the accurate determination of contamination liability.…”

Section: Introductionmentioning

confidence: 99%

Groundwater contamination source identification based on Sobol Sequences-based Sparrow Search Algorithm with a BiLSTM surrogate model

Pan

2022

Preprint

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In the traditional linked simulation-optimization method, solving the optimization model requires massive invoking of the groundwater numerical simulation model, which causes a huge computational load. In the present study, a surrogate model of the origin simulation model was developed using a Bidirectional Long and Short-term Memory neural network method (BiLSTM). Compared with the surrogate models built by shallow learning methods (BP neural network) and traditional LSTM methods, the surrogate model built by BiLSTM has higher accuracy and better generalization performance while reducing the computational load. The BiLSTM surrogate model was linked to the optimization model and solved using the Sparrow Search Algorithm based on Sobol sequences (SSAS). SSAS enhances the diversity of the initial population of sparrows by introducing Sobol sequences and introduces nonlinear inertia weights to control the search range and search efficiency. Compared with SSA, SSAS has stronger global search ability and faster search efficiency. And SSAS identifies the contamination source location and release intensity stably and reliably. This study also applied the Cholesky decomposition method to establish a Gaussian field for hydraulic conductivity to evaluate the feasibility of the simulation-optimization method.

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Section: Introductionmentioning

confidence: 99%

Groundwater contamination source identification based on Sobol Sequences-based Sparrow Search Algorithm with a BiLSTM surrogate model

Pan

2022

Preprint

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“…The first step in groundwater CCL4-pollution control is to trace the source of pollutants, which is to find out the location of pollution sources and the history of pollutant migration and transformation, through limited observational-data [10,11]. Groundwater-tracing techniques mainly include the geochemical-footprint method and numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Carbon Tetrachloride Pollution-Traceability in Groundwater System of an Industrial City

et al. 2022

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Carbon Tetrachloride (CCL4) is a colorless, volatile, and toxic liquid. Once it pollutes groundwater, it will not only destroy the ecological environment but also negatively affect the functioning of the human liver. An industrial city in eastern China has been contaminated with carbon tetrachloride (CCL4). Due to the complex hydrogeological conditions, it is difficult to determine the pollution source by a single hydrochemical analysis. In order to solve the traceability problem, in this work we established a traceability system by combining hydrochemical analysis, backward tracing, and forward transport, and analyzed the pollution distribution, pollution-source location and pollution-transport characteristic in groundwater, which provided technical support for CCL4-pollution control of groundwater in this area. (1) Groundwater samples were analyzed using the gas chromatography/mass spectrometry method. Through the pollution concentration-field, we identified the location of the pollution center and the concentration value in the northeast and southwest, screening out the monitoring wells exceeding the criteria: HF#1, DJ#19, DJ#7, SS#4, ZF#2, and DY#3. (2) Backward tracing over the past 30 years and 50 years was conducted through MODPATH. Potential polluting factories were identified by comparing the capture area with the historical distribution of factories. (3) Forward solute-transport was performed at the location of these potentially polluted factories. The distribution characteristics of pollutants in limestone layers and the Quaternary layer were analyzed using a simulated concentration-field for August 2012 and October 2016. (4) Comparing the simulated concentration with the observed concentration, the source of CCL4 pollution was inferred to be the textile mill in the 1990s, the steelworks in the 1970s, and the machine-tool plant in the 1970s and 1990s. Based on the concentration–duration curve at the pollution source, the transport characteristics and the transport speed of the pollutants in the study area were analyzed. This work not only successfully found the location of CCL4-pollution sources, but also helped the local government to analyze the year of pollutant release and recognize the transport pattern of CCL4 in aquifers.

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Groundwater contamination source identification based on Sobol sequences–based sparrow search algorithm with a BiLSTM surrogate model

Pan

2023

Environ Sci Pollut Res

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Optimal Unknown Pollution Source Characterization in a Contaminated Groundwater Aquifer—Evaluation of a Developed Dedicated Software Tool

Cited by 6 publications

References 34 publications

Groundwater contamination source identification based on Sobol Sequences-based Sparrow Search Algorithm with a BiLSTM surrogate model

Groundwater contamination source identification based on Sobol Sequences-based Sparrow Search Algorithm with a BiLSTM surrogate model

Numerical Simulation of Carbon Tetrachloride Pollution-Traceability in Groundwater System of an Industrial City

Groundwater contamination source identification based on Sobol sequences–based sparrow search algorithm with a BiLSTM surrogate model

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