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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Groundwater contaminant source characterization with simulation model parameter estimation utilizing a heuristic

Han Wang1, Wenxi Lu1, Jiuhui Li1

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin Univ., Changchun 130021, Chin; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin Univ., Changchun 130021, China; College of New Energy and Environment, Jilin Univ., Changchun 130021, China.

Journal of Contaminant Hydrology
|August 3, 2020
PubMed
Summary
This summary is machine-generated.

A new heuristic search strategy aids groundwater contaminant source characterization by improving simulation model parameter estimation. This method enhances accuracy using a multi-kernel extreme learning machine surrogate system and self-adaptive feedback correction.

Keywords:
Groundwater contaminant characterizationHeuristic search strategyMK-ELM surrogate systemSelf-adaptive feedback correction stepStochastic-simulation statistic method

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Computational Modeling

Background:

  • Groundwater contamination poses significant environmental risks, necessitating accurate source characterization.
  • Traditional methods for groundwater contaminant source characterization (GCSC) often involve computationally intensive simulations.
  • Parameter estimation for simulation models is crucial for effective GCSC but computationally demanding.

Purpose of the Study:

  • To develop an efficient heuristic search strategy for GCSC.
  • To improve the accuracy of simulation model parameter estimation in GCSC.
  • To reduce the computational load associated with GCSC using surrogate modeling.

Main Methods:

  • Development of a heuristic search strategy based on the stochastic-simulation statistic (S-S) approach.
  • Implementation of a multi-kernel extreme learning machine (MK-ELM) as a surrogate for the numerical simulation model.
  • Integration of a self-adaptive feedback correction step within the heuristic search iterative process.

Main Results:

  • The MK-ELM surrogate system demonstrated improved approximation accuracy compared to single KELM.
  • The self-adaptive sampling and feedback correction significantly enhanced the efficiency and accuracy of parameter estimation.
  • The heuristic search strategy successfully assisted in GCSC within a hypothetical case study.

Conclusions:

  • The proposed heuristic search strategy, coupled with MK-ELM and self-adaptive feedback, is effective for GCSC.
  • This approach offers a computationally efficient and accurate method for simulation model parameter estimation.
  • The developed strategy provides a valuable tool for environmental scientists and hydrogeologists in addressing groundwater contamination issues.