Efficient Bayesian experimental design for contaminant source identification

Zhang, Jiangjiang; Zeng, Lingzao; Chen, Cheng; Chen, Dingjiang; Wu, Laosheng

doi:10.1002/2014wr015740

Cited by 113 publications

(96 citation statements)

References 63 publications

(80 reference statements)

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“…Formulating the inverse problem in a probabilistic framework, Bayesian methods can be used to accurately estimate the unknown parameters and associated uncertainties. As a very popular Bayesian method, Markov chain Monte Carlo (MCMC) requires repeated evaluations of the governing equations to generate posterior parameter samples (Scholer et al, 2012; Zhang et al, 2015; Shi et al, 2014). If the numerical solver is computationally demanding, the computational cost of MCMC simulation will be prohibitive.…”

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confidence: 99%

Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils

Gao

Zhang

Liu

et al. 2019

Vadose Zone Journal

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Core Ideas The adaptive GP‐based MCMC was efficient to estimate hydraulic parameters in soils. Accuracy of the estimated parameters was verified by simulating experimental results. These simulations revealed a significant effect of layered structure on soil water flow. Modeling water movement in heterogeneous soils, e.g., layered soils, is an essential but challenging task that requires accurate estimation of multiple sets of soil hydraulic parameters. Markov chain Monte Carlo (MCMC) is a popular but computationally expensive method for parameter estimation. An adaptive Gaussian process (GP)‐based MCMC method proposed in our previous work presents significant computational efficiency. Nevertheless, its performance was evaluated only for synthetic numerical cases and has not been experimentally validated. Furthermore, its applicability in estimating hydraulic parameters of layered soils is still unknown. In this study, we systematically evaluated the performance of the GP‐based MCMC method in estimating the layered soil hydraulic parameters through a water infiltration experiment. It was shown that the proposed method could provide reliable estimations that were very close to those given by the original‐model‐based MCMC but at a much lower computational cost. The simulated soil water dynamics using the estimated parameters revealed a significant effect of layered heterogeneity on water flow. The lower layer(s) with higher water suction may cause persistent unsaturated status of the upper layer(s) during infiltration.

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confidence: 99%

Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils

Gao

Zhang

Liu

et al. 2019

Vadose Zone Journal

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“…Little effort has been made to estimate the source probabilities [127]. An efficient experimental design for contamination source identification in ground water was discussed by Zhang et al [148]. Bayesian techniques have become a probable means for the CSI challenge by openly allocate probabilities to the likely source locations.…”

Section: Probabilistic Approachmentioning

confidence: 99%

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

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Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers' taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

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“…A more general way is to implement the experimental design in the Bayesian framework, where all quantities of interest are modeled as random variables with corresponding probabilistic distributions and any higher-order statistics are readily available. Zhang et al (2015Zhang et al ( , 2016 designed the optimal monitoring locations for groundwater contaminants through maximizing the expected relative entropy. An assumption-free Bayesian design criterion, that is, the expected relative entropy between the prior and posterior distributions (also known as the Kullback-Leibler divergence ;Lindley 1956), has been used in groundwater monitoring design.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Zhang

et al. 2018

Groundwater

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Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.

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Efficient Bayesian experimental design for contaminant source identification

Cited by 113 publications

References 63 publications

Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils

Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

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