Reconstructing the release history of a groundwater contaminant based on AT123D

Yeh, Hund‐Der; Lin, Chao-Chih; Chen, Chi-Fen

doi:10.1016/j.jher.2015.06.001

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…Of the papers addressing a volumetric source, all assume that the shape is known, except for Mahinthakumar and Sayeed (2006), Mirghani et al (2009), Aghasi et al (2013), Jin et al (2014), and Yeh et al (2016, who also attempt to identify the shape of the source, with most using a simple prismatic parameterization.…”

Section: Sourcementioning

confidence: 99%

Contaminant Source Identification in Aquifers: A Critical View

Gómez‐Hernández

2021

Math Geosci

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Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.

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Section: Sourcementioning

confidence: 99%

Contaminant Source Identification in Aquifers: A Critical View

Gómez‐Hernández

2021

Math Geosci

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“…This method provides good results in the identification of sources of pollution that affect the quality of groundwater (Ayvaz 2016). The choice for the SA algorithm is recommended by some authors since it eliminates the unstable numerical feature of the solution schemes of the inverse problem (Newman et al 2005;Yeh et al 2007;Amirabdollahian and Datta 2014;Ayvaz 2016;Yeh et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Particle filter-based data assimilation technique for the evaluation of transport of pollutants in small rivers

et al. 2020

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The discharge of pollutants into water resources has caused many undesirable effects on human health and the environment. In this context, meta-heuristic methods, such as the Simulated Annealing algorithm, allow for only offline estimation of the concentration and dispersion coefficient of pollutant. However, online data assimilation is a very important for decision support in real time. In this regard, the present paper focuses on the use of the so-called Particle Filter as an online observer. Our studies were performed through the algorithms Sampling Importance Resampling (SIR) and Liu and West (LW), using tracer experimental observations from a small river in Brazil. Such approach allowed for tracking satisfactorily the pollutant concentration, longitudinal dispersion coefficient, flow velocity, pollutant load released and distance from the pollution source. The results can be enhanced ever more through the LW filter, which reduces the impoverishment of the samples as well as the computational time due to the use of a smoothing kernel.

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