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Kenneth W Harrison - One of the best experts on this subject based on the ideXlab platform.
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improving efficiency of the bayesian approach to water distribution Contaminant Source characterization with support vector regression
Journal of Water Resources Planning and Management, 2014Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:AbstractThere are multiple Sources of uncertainties in urban water-distribution systems, e.g., nodal water demand and sensor measurement error. All of these uncertainties increase the complexity of Contaminant Source identification in a sparse sensor network. The large number of attributes (e.g., Contaminant Source location, magnitude, injection starting time, and duration) of a Contaminant event profile cannot be identified given limited sensor data. Instead, the uncertainties in the Contaminant event profile need to be characterized. Markov chain Monte Carlo (MCMC) methods for Bayesian analyses allow for the characterization of the uncertainty in the contamination event profile. To account for stochastic water demands, which has been shown in some circumstances to be necessary if the Contaminant event is to be properly characterized, the evaluation of the likelihood function is the most computationally expensive part of the MCMC implementation. Previous work applied Monte Carlo methods for error propaga...
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bayesian approach to Contaminant Source characterization in water distribution systems adaptive sampling framework
Stochastic Environmental Research and Risk Assessment, 2013Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:Bayesian analysis can yield a probabilistic Contaminant Source characterization conditioned on available sensor data and accounting for system stochastic processes. This paper is based on a previously proposed Markov chain Monte Carlo (MCMC) approach tailored for water distribution systems and incorporating stochastic water demands. The observations can include those from fixed sensors and, the focus of this paper, mobile sensors. Decision makers, such as utility managers, need not wait until new observations are available from an existing sparse network of fixed sensors. This paper addresses a key research question: where is the best location in the network to gather additional measurements so as to maximize the reduction in the Source uncertainty? Although this has been done in groundwater management, it has not been well addressed in water distribution networks. In this study, an adaptive framework is proposed to guide the strategic placement of mobile sensors to complement the fixed sensor network. MCMC is the core component of the proposed adaptive framework, while several other pieces are indispensable: Bayesian preposterior analysis, value of information criterion and the search strategy for identifying an optimal location. Such a framework is demonstrated with an illustrative example, where four candidate sampling locations in the small water distribution network are investigated. Use of different value-of-information criteria reveals that while each may lead to different outcomes, they share some common characteristics. The results demonstrate the potential of Bayesian analysis and the MCMC method for Contaminant event management.
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bayesian update method for Contaminant Source characterization in water distribution systems
Journal of Water Resources Planning and Management, 2013Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:AbstractBayesian analysis has application to probabilistic Source characterization in water distribution systems. A new implementation of Markov-chain Monte Carlo (MCMC) for this problem is described. The solution addresses the discrete nature of water distribution networks that precludes the application of MCMC methods of general applicability that have been reported elsewhere in the water reSources literature. The method is applied to a hypothetical network that has been used by others to test Source identification methods. The likelihood function, a key component of Bayes’ rule, is evaluated using a Monte Carlo–based stochastic water-demand model. The results reinforce the need to address the multiple Sources of uncertainty in the Source characterization, including the stochastic variation of water demand. Further research is needed to make the approach feasible in operational environments. Limitations of the approach and future research directions are discussed.
Hui Wang - One of the best experts on this subject based on the ideXlab platform.
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improving efficiency of the bayesian approach to water distribution Contaminant Source characterization with support vector regression
Journal of Water Resources Planning and Management, 2014Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:AbstractThere are multiple Sources of uncertainties in urban water-distribution systems, e.g., nodal water demand and sensor measurement error. All of these uncertainties increase the complexity of Contaminant Source identification in a sparse sensor network. The large number of attributes (e.g., Contaminant Source location, magnitude, injection starting time, and duration) of a Contaminant event profile cannot be identified given limited sensor data. Instead, the uncertainties in the Contaminant event profile need to be characterized. Markov chain Monte Carlo (MCMC) methods for Bayesian analyses allow for the characterization of the uncertainty in the contamination event profile. To account for stochastic water demands, which has been shown in some circumstances to be necessary if the Contaminant event is to be properly characterized, the evaluation of the likelihood function is the most computationally expensive part of the MCMC implementation. Previous work applied Monte Carlo methods for error propaga...
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characterization of groundwater Contaminant Source using bayesian method
Stochastic Environmental Research and Risk Assessment, 2013Co-Authors: Hui WangAbstract:Contaminant Source identification in groundwater system is critical for remediation strategy implementation, including gathering further samples and analysis, as well as implementing and evaluating different remediation plans. Such problem is usually solved with the aid of groundwater modeling with lots of uncertainty, e.g. existing uncertainty in hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This paper presents an application of a dynamic framework coupling with a three dimensional groundwater modeling scheme in contamination Source identification of groundwater. Markov Chain Monte Carlo (MCMC) is being applied to infer the possible location and magnitude of contamination Source. Uncertainty existing in heterogonous hydraulic conductivity field is explicitly considered in evaluating the likelihood function. Unlike other inverse-problem approaches to provide single but maybe untrue solution, the MCMC algorithm provides probability distributions over estimated parameters. Results from this algorithm offer a probabilistic inference of the location and concentration of released contamination. The convergence analysis of MCMC reveals the effectiveness of the proposed algorithm. Further investigation to extend this study is also discussed.
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bayesian approach to Contaminant Source characterization in water distribution systems adaptive sampling framework
Stochastic Environmental Research and Risk Assessment, 2013Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:Bayesian analysis can yield a probabilistic Contaminant Source characterization conditioned on available sensor data and accounting for system stochastic processes. This paper is based on a previously proposed Markov chain Monte Carlo (MCMC) approach tailored for water distribution systems and incorporating stochastic water demands. The observations can include those from fixed sensors and, the focus of this paper, mobile sensors. Decision makers, such as utility managers, need not wait until new observations are available from an existing sparse network of fixed sensors. This paper addresses a key research question: where is the best location in the network to gather additional measurements so as to maximize the reduction in the Source uncertainty? Although this has been done in groundwater management, it has not been well addressed in water distribution networks. In this study, an adaptive framework is proposed to guide the strategic placement of mobile sensors to complement the fixed sensor network. MCMC is the core component of the proposed adaptive framework, while several other pieces are indispensable: Bayesian preposterior analysis, value of information criterion and the search strategy for identifying an optimal location. Such a framework is demonstrated with an illustrative example, where four candidate sampling locations in the small water distribution network are investigated. Use of different value-of-information criteria reveals that while each may lead to different outcomes, they share some common characteristics. The results demonstrate the potential of Bayesian analysis and the MCMC method for Contaminant event management.
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bayesian update method for Contaminant Source characterization in water distribution systems
Journal of Water Resources Planning and Management, 2013Co-Authors: Hui Wang, Kenneth W HarrisonAbstract:AbstractBayesian analysis has application to probabilistic Source characterization in water distribution systems. A new implementation of Markov-chain Monte Carlo (MCMC) for this problem is described. The solution addresses the discrete nature of water distribution networks that precludes the application of MCMC methods of general applicability that have been reported elsewhere in the water reSources literature. The method is applied to a hypothetical network that has been used by others to test Source identification methods. The likelihood function, a key component of Bayes’ rule, is evaluated using a Monte Carlo–based stochastic water-demand model. The results reinforce the need to address the multiple Sources of uncertainty in the Source characterization, including the stochastic variation of water demand. Further research is needed to make the approach feasible in operational environments. Limitations of the approach and future research directions are discussed.
Yuqiao Long - One of the best experts on this subject based on the ideXlab platform.
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groundwater Contaminant Source identification via bayesian model selection and uncertainty quantification
Hydrogeology Journal, 2019Co-Authors: Xiankui Zeng, Jichun Wu, Dong Wang, Yuqiao LongAbstract:Many deterministic and stochastic approaches have been applied for groundwater Contaminant Source identification (GCSI) in recent decades. Usually, these implementations are based on a single groundwater model or fixed model structure and ignore the uncertainty of model structure. However, model structure uncertainty is inevitable for groundwater modeling, especially for complex geological environments and limited observations. This study evaluated the impact of model structure uncertainty on GCSI, and proposes an approach for GCSI based on Bayesian model selection. In the framework of multiple model analysis, a set of alternative model structures are used to represent the unknown groundwater system. Then, a novel nested sampling algorithm, POLYCHORD, is used for model selection and Source identification. This algorithm is capable of estimating the model’s marginal likelihood and inferring the posterior distribution of the Contaminant Source’s characteristics simultaneously. Finally, this proposed approach is verified through two GCSI case studies, which include a synthetic groundwater contamination problem and a groundwater transport column experiment. The results demonstrated that GCSI could be inconsistent when using different model structures. Models with higher marginal likelihoods tend to have better performance on the predictions of the Contaminant Source’s characteristics. It was concluded that POLYCHORD is efficient in marginal likelihood estimation and GCSI.
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Groundwater Contaminant Source identification via Bayesian model selection and uncertainty quantification
Hydrogeology Journal, 2019Co-Authors: Tongtong Cao, Xiankui Zeng, Yuanyuan Sun, Xiaobin Zhu, Jin Lin, Jichun Wu, Dong Wang, Yuqiao LongAbstract:摘要近年来已有大量的确定性和随机性方法用于地下水污染源识别,通常这些方法是基于单一的地下水模型或固定的模型结构,而未考虑模型结构的不确定性。在实际条件下,特别对于复杂的地质环境和有限的观测数据,地下水模型的结构不确定性是不可避免的。本次研究评价了模型结构不确定性对于地下水污染源识别的影响,并提出一种基于贝叶斯模型选择的污染源识别方法。首先,在多模型分析的框架内,提出一组可行的备选模型结构来描述实际未知的地下水系统;其次,将最新提出的POLYCHORD算法用于贝叶斯模型选择及污染源识别。最后,通过两个案例分析,包括一个理想的地下水污染物运移案例和一个地下水污染物运移室内实验,对提出的污染源识别方法进行验证。研究结果表明,不同的模型结构将会导致不一致的地下水污染源识别结果。具有越高模型边缘似然值的模型结构,能够更好的进行污染源识别。同时,提出的POLYCHORD算法能够有效地进行模型边缘似然值估计和地下水污染源识别。AbstractMany deterministic and stochastic approaches have been applied for groundwater Contaminant Source identification (GCSI) in recent decades. Usually, these implementations are based on a single groundwater model or fixed model structure and ignore the uncertainty of model structure. However, model structure uncertainty is inevitable for groundwater modeling, especially for complex geological environments and limited observations. This study evaluated the impact of model structure uncertainty on GCSI, and proposes an approach for GCSI based on Bayesian model selection. In the framework of multiple model analysis, a set of alternative model structures are used to represent the unknown groundwater system. Then, a novel nested sampling algorithm, POLYCHORD, is used for model selection and Source identification. This algorithm is capable of estimating the model’s marginal likelihood and inferring the posterior distribution of the Contaminant Source’s characteristics simultaneously. Finally, this proposed approach is verified through two GCSI case studies, which include a synthetic groundwater contamination problem and a groundwater transport column experiment. The results demonstrated that GCSI could be inconsistent when using different model structures. Models with higher marginal likelihoods tend to have better performance on the predictions of the Contaminant Source’s characteristics. It was concluded that POLYCHORD is efficient in marginal likelihood estimation and GCSI.ResumoMuitas abordagens determinísticas e estocásticas foram aplicadas na identificação de fontes de Contaminantes de águas subterrâneas (IFCAS) em décadas recentes. Geralmente, essas implementações são baseadas em um único modelo de águas subterrâneas ou um modelo de estrutura fixa e ignoram a incerteza da estrutura do modelo. Entretanto, a incerteza da estrutura do modelo é inevitável para modelagem das águas subterrâneas, especialmente para ambientes geológicos complexos e observações limitadas. Esse estudo avaliou o impacto da incerteza da estrutura do modelo na IFCAS, e propõe uma abordagem para IFCAS baseada na seleção Bayesiana de modelos. Na estrutura da análise de modelos múltiplos, um conjunto de estruturas de modelos alternativos é usado para representar o sistema de água subterrânea desconhecido. Em seguida, um novo algoritmo de amostragem aninhada, POLYCHORD, é usado para seleção do modelo e identificação de fonte. Esse algoritmo é capaz de estimar a probabilidade marginal do modelo e inferir a distribuição posterior das características da fonte Contaminante simultaneamente. Finalmente, essa abordagem proposta é verificada através de dois estudos de caso de IFCAS, que incluem um problema sintético de contaminação das águas subterrâneas e um experimento de coluna de transporte de águas subterrâneas. Os resultados demonstraram que a IFCAS pode ser inconsistente ao usar diferentes estruturas de modelo. Modelos com maiores probabilidades marginais tendem a ter melhor desempenho nas previsões das características da fonte de Contaminantes. Concluiu-se que o POLYCHORD é eficiente na estimativa da probabilidade marginal e na IFCAS.ResumenEn las últimas décadas se han aplicado muchos enfoques deterministas y estocásticos para la identificación de fuentes de Contaminantes de aguas subterráneas (GCSI). Por lo general, estas implementaciones se basan en un modelo único de agua subterránea o en una estructura de modelo fijo e ignoran la incertidumbre de la estructura del modelo. Sin embargo, la incertidumbre de la estructura del modelo es inevitable para el modelado de aguas subterráneas, especialmente para entornos geológicos complejos y observaciones limitadas. Este estudio evaluó el impacto de la incertidumbre de la estructura del modelo en la GCSI, y propone un enfoque para la GCSI basado en la selección del modelo bayesiano. En el marco del análisis de modelos múltiples, se utiliza un conjunto de estructuras de modelos alternativos para representar el sistema de aguas subterráneas desconocido. A continuación, se utiliza un nuevo algoritmo de muestreo anidado, POLYCHORD, para la selección del modelo y la identificación de la fuente. Este algoritmo es capaz de estimar la probabilidad marginal del modelo e inferir la distribución posterior de las características de la fuente del Contaminante simultáneamente. Finalmente, este enfoque propuesto se verifica a través de dos estudios de caso de la GCSI, que incluyen un problema de contaminación sintética de las aguas subterráneas y un experimento de columna de transporte de aguas subterráneas. Los resultados demostraron que la GCSI podría ser inconsistente cuando se utilizan diferentes estructuras de modelos. Los modelos con mayor probabilidad marginal tienden a tener un mejor desempeño en las predicciones de las características de la fuente del Contaminante. Se concluyó que POLYCHORD es eficiente en la estimación de la probabilidad marginal y en la GCSI.RésuméDe nombreuses approches déterministes et stochastiques ont été utilisées pour identifier la Source de Contaminants dans les eaux souterraines au cours des dernières décennies. Habituellement, leurs mises en œuvre sont basés sur un modèle hydrogéologique unique ou une structure de modèle fixe et ignorent l’incertitude de la structure du modèle. Cependant, l’incertitude de la structure du modèle est inévitable dans le cas de la modélisation hydrogéologique, en particulier pour des environnements géologiques complexes et des observations limitées. Cette étude a évalué l’impact de l’incertitude liée à la structure du modèle sur l’identification de la Source de Contaminants dans les eaux souterraines, et propose une approche pour le faire qui est basée sur la sélection d’un modèle bayésien. Dans le cadre d’analyse de multiple modèles, un ensemble de structures de modèle alternatif est utilisé pour représenter la partie non connue du système hydrogéologique. Ensuite, un nouvel algorithme d’échantillonnage imbriqué, POLYCHORD, est utilisé pour la sélection du modèle et l’identification de la Source. Cet algorithme est capable d’estimer la probabilité marginale du modèle et d’interférer avec la distribution à postériori des caractéristiques de la Source des Contaminants simultanément. Finalement, cette approche proposée est vérifiée en l’appliquant sur deux cas d’études, qui intègrent un problème synthétique de contamination des eaux souterraines et une expérience de transport dans l’eau souterraine sur une colonne. Les résultats démontrent que l’identification de la Source des Contaminants des eaux souterraines pourrait être incohérente si l’on utilise des structures de modèle différentes. Les modèles dont les probabilités marginales sont les plus élevées, ont tendance à être plus performant sur les prévisions des caractéristiques de la Source des Contaminants. Il a été conclu que POLYCHORD est efficace dans l’estimation da la probabilité marginale et l’identification de la Source des Contaminants dans les eaux souterraines.
Jichun Wu - One of the best experts on this subject based on the ideXlab platform.
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groundwater Contaminant Source identification via bayesian model selection and uncertainty quantification
Hydrogeology Journal, 2019Co-Authors: Xiankui Zeng, Jichun Wu, Dong Wang, Yuqiao LongAbstract:Many deterministic and stochastic approaches have been applied for groundwater Contaminant Source identification (GCSI) in recent decades. Usually, these implementations are based on a single groundwater model or fixed model structure and ignore the uncertainty of model structure. However, model structure uncertainty is inevitable for groundwater modeling, especially for complex geological environments and limited observations. This study evaluated the impact of model structure uncertainty on GCSI, and proposes an approach for GCSI based on Bayesian model selection. In the framework of multiple model analysis, a set of alternative model structures are used to represent the unknown groundwater system. Then, a novel nested sampling algorithm, POLYCHORD, is used for model selection and Source identification. This algorithm is capable of estimating the model’s marginal likelihood and inferring the posterior distribution of the Contaminant Source’s characteristics simultaneously. Finally, this proposed approach is verified through two GCSI case studies, which include a synthetic groundwater contamination problem and a groundwater transport column experiment. The results demonstrated that GCSI could be inconsistent when using different model structures. Models with higher marginal likelihoods tend to have better performance on the predictions of the Contaminant Source’s characteristics. It was concluded that POLYCHORD is efficient in marginal likelihood estimation and GCSI.
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Groundwater Contaminant Source identification via Bayesian model selection and uncertainty quantification
Hydrogeology Journal, 2019Co-Authors: Tongtong Cao, Xiankui Zeng, Yuanyuan Sun, Xiaobin Zhu, Jin Lin, Jichun Wu, Dong Wang, Yuqiao LongAbstract:摘要近年来已有大量的确定性和随机性方法用于地下水污染源识别,通常这些方法是基于单一的地下水模型或固定的模型结构,而未考虑模型结构的不确定性。在实际条件下,特别对于复杂的地质环境和有限的观测数据,地下水模型的结构不确定性是不可避免的。本次研究评价了模型结构不确定性对于地下水污染源识别的影响,并提出一种基于贝叶斯模型选择的污染源识别方法。首先,在多模型分析的框架内,提出一组可行的备选模型结构来描述实际未知的地下水系统;其次,将最新提出的POLYCHORD算法用于贝叶斯模型选择及污染源识别。最后,通过两个案例分析,包括一个理想的地下水污染物运移案例和一个地下水污染物运移室内实验,对提出的污染源识别方法进行验证。研究结果表明,不同的模型结构将会导致不一致的地下水污染源识别结果。具有越高模型边缘似然值的模型结构,能够更好的进行污染源识别。同时,提出的POLYCHORD算法能够有效地进行模型边缘似然值估计和地下水污染源识别。AbstractMany deterministic and stochastic approaches have been applied for groundwater Contaminant Source identification (GCSI) in recent decades. Usually, these implementations are based on a single groundwater model or fixed model structure and ignore the uncertainty of model structure. However, model structure uncertainty is inevitable for groundwater modeling, especially for complex geological environments and limited observations. This study evaluated the impact of model structure uncertainty on GCSI, and proposes an approach for GCSI based on Bayesian model selection. In the framework of multiple model analysis, a set of alternative model structures are used to represent the unknown groundwater system. Then, a novel nested sampling algorithm, POLYCHORD, is used for model selection and Source identification. This algorithm is capable of estimating the model’s marginal likelihood and inferring the posterior distribution of the Contaminant Source’s characteristics simultaneously. Finally, this proposed approach is verified through two GCSI case studies, which include a synthetic groundwater contamination problem and a groundwater transport column experiment. The results demonstrated that GCSI could be inconsistent when using different model structures. Models with higher marginal likelihoods tend to have better performance on the predictions of the Contaminant Source’s characteristics. It was concluded that POLYCHORD is efficient in marginal likelihood estimation and GCSI.ResumoMuitas abordagens determinísticas e estocásticas foram aplicadas na identificação de fontes de Contaminantes de águas subterrâneas (IFCAS) em décadas recentes. Geralmente, essas implementações são baseadas em um único modelo de águas subterrâneas ou um modelo de estrutura fixa e ignoram a incerteza da estrutura do modelo. Entretanto, a incerteza da estrutura do modelo é inevitável para modelagem das águas subterrâneas, especialmente para ambientes geológicos complexos e observações limitadas. Esse estudo avaliou o impacto da incerteza da estrutura do modelo na IFCAS, e propõe uma abordagem para IFCAS baseada na seleção Bayesiana de modelos. Na estrutura da análise de modelos múltiplos, um conjunto de estruturas de modelos alternativos é usado para representar o sistema de água subterrânea desconhecido. Em seguida, um novo algoritmo de amostragem aninhada, POLYCHORD, é usado para seleção do modelo e identificação de fonte. Esse algoritmo é capaz de estimar a probabilidade marginal do modelo e inferir a distribuição posterior das características da fonte Contaminante simultaneamente. Finalmente, essa abordagem proposta é verificada através de dois estudos de caso de IFCAS, que incluem um problema sintético de contaminação das águas subterrâneas e um experimento de coluna de transporte de águas subterrâneas. Os resultados demonstraram que a IFCAS pode ser inconsistente ao usar diferentes estruturas de modelo. Modelos com maiores probabilidades marginais tendem a ter melhor desempenho nas previsões das características da fonte de Contaminantes. Concluiu-se que o POLYCHORD é eficiente na estimativa da probabilidade marginal e na IFCAS.ResumenEn las últimas décadas se han aplicado muchos enfoques deterministas y estocásticos para la identificación de fuentes de Contaminantes de aguas subterráneas (GCSI). Por lo general, estas implementaciones se basan en un modelo único de agua subterránea o en una estructura de modelo fijo e ignoran la incertidumbre de la estructura del modelo. Sin embargo, la incertidumbre de la estructura del modelo es inevitable para el modelado de aguas subterráneas, especialmente para entornos geológicos complejos y observaciones limitadas. Este estudio evaluó el impacto de la incertidumbre de la estructura del modelo en la GCSI, y propone un enfoque para la GCSI basado en la selección del modelo bayesiano. En el marco del análisis de modelos múltiples, se utiliza un conjunto de estructuras de modelos alternativos para representar el sistema de aguas subterráneas desconocido. A continuación, se utiliza un nuevo algoritmo de muestreo anidado, POLYCHORD, para la selección del modelo y la identificación de la fuente. Este algoritmo es capaz de estimar la probabilidad marginal del modelo e inferir la distribución posterior de las características de la fuente del Contaminante simultáneamente. Finalmente, este enfoque propuesto se verifica a través de dos estudios de caso de la GCSI, que incluyen un problema de contaminación sintética de las aguas subterráneas y un experimento de columna de transporte de aguas subterráneas. Los resultados demostraron que la GCSI podría ser inconsistente cuando se utilizan diferentes estructuras de modelos. Los modelos con mayor probabilidad marginal tienden a tener un mejor desempeño en las predicciones de las características de la fuente del Contaminante. Se concluyó que POLYCHORD es eficiente en la estimación de la probabilidad marginal y en la GCSI.RésuméDe nombreuses approches déterministes et stochastiques ont été utilisées pour identifier la Source de Contaminants dans les eaux souterraines au cours des dernières décennies. Habituellement, leurs mises en œuvre sont basés sur un modèle hydrogéologique unique ou une structure de modèle fixe et ignorent l’incertitude de la structure du modèle. Cependant, l’incertitude de la structure du modèle est inévitable dans le cas de la modélisation hydrogéologique, en particulier pour des environnements géologiques complexes et des observations limitées. Cette étude a évalué l’impact de l’incertitude liée à la structure du modèle sur l’identification de la Source de Contaminants dans les eaux souterraines, et propose une approche pour le faire qui est basée sur la sélection d’un modèle bayésien. Dans le cadre d’analyse de multiple modèles, un ensemble de structures de modèle alternatif est utilisé pour représenter la partie non connue du système hydrogéologique. Ensuite, un nouvel algorithme d’échantillonnage imbriqué, POLYCHORD, est utilisé pour la sélection du modèle et l’identification de la Source. Cet algorithme est capable d’estimer la probabilité marginale du modèle et d’interférer avec la distribution à postériori des caractéristiques de la Source des Contaminants simultanément. Finalement, cette approche proposée est vérifiée en l’appliquant sur deux cas d’études, qui intègrent un problème synthétique de contamination des eaux souterraines et une expérience de transport dans l’eau souterraine sur une colonne. Les résultats démontrent que l’identification de la Source des Contaminants des eaux souterraines pourrait être incohérente si l’on utilise des structures de modèle différentes. Les modèles dont les probabilités marginales sont les plus élevées, ont tendance à être plus performant sur les prévisions des caractéristiques de la Source des Contaminants. Il a été conclu que POLYCHORD est efficace dans l’estimation da la probabilité marginale et l’identification de la Source des Contaminants dans les eaux souterraines.
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deep autoregressive neural networks for high dimensional inverse problems in groundwater Contaminant Source identification
Water Resources Research, 2019Co-Authors: Nicholas Zabaras, Shaoxing Mo, Jichun WuAbstract:Identification of a groundwater Contaminant Source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem. In this study, a deep autoregressive neural network-based surrogate method is developed for the forward model to allow us to solve efficiently such high-dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time-varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats the output at the previous time step as input to the network for predicting the output at the current time step. We employ a dense convolutional encoder-decoder network architecture in which the high-dimensional input and output fields of the model are treated as images to leverage the robust capability of convolutional networks in image-like data processing. An iterative local updating ensemble smoother (ILUES) algorithm is used as the inversion framework. The proposed method is evaluated using a synthetic Contaminant Source identification problem with 686 uncertain input parameters. Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high-dimensional model input-output relationship. The autoregressive strategy substantially improves the network's accuracy and computational efficiency. The application of the surrogate-based ILUES in solving the inverse problem shows that it can achieve accurate inversion results and predictive uncertainty estimates.
Xianting Li - One of the best experts on this subject based on the ideXlab platform.
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REAL-TIME DETERMINATION OF INDOOR Contaminant Source LOCATION AND STRENGTH, PART II: WITH TWO SENSORS
2020Co-Authors: Xianting Li, Weiding Long, Xibin MaAbstract:In the preceding companion paper (Part I), a method with one sensor that could identify the indoor Contaminant Source location and strength in short time was presented. On the basis of further theoretical study, a method with two sensors is presented in this paper to identify Contaminant Source with higher accuracy. This paper demonstrates how to use the method with two sensors to find the location of Contaminant Source in a threedimensional room. In addition, the accuracy of two types of methods was compared. The correctness probability, which are used to evaluate the accuracy of Source locating, of the method with two sensors and the method with one sensor are 83.3% and 94.8% respectively. The results show that the method with two sensors works better for locating Contaminant Source than that with one sensor. In practice, the method with two sensors may be more applicable for the situations where the accuracy of Source identification is crucial while the costs of sensors are not of great concern.
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Quantitative effects of supply air and Contaminant Sources on steady Contaminant distribution in ventilated space with air recirculation
Building and Environment, 2020Co-Authors: Xiaoliang Shao, Xianting Li, Shukui Liang, Chao LiangAbstract:Abstract Identifying the contributions of different influencing factors to indoor air environment is beneficial for seeking measures to improve environmental quality. The independent effect of supply air and Contaminant Source can be quantified using accessibility index; however, for ventilated rooms with air recirculation devices, the effects of supply air and Contaminant Source cannot be described directly owing to the effect of air recirculation. In this study, a concise linear expression is derived to establish a correlation between steady Contaminant distribution and each of independent supply air and Contaminant Sources with air recirculation, based on which index of revised accessibility is defined to evaluate the effects of supply air and Contaminant Sources. The reliability of the proposed method is verified by comparing with simulation result. Two numerical cases, i.e., two air recirculation devices with cleaning efficiency and a recirculated air curtain, are analyzed to study the quantitative effects of supply air and Contaminant Source. It is discovered that air recirculation devices change the airflow field considerably, and that the purification effect of air recirculation devices with cleaning efficiencies of 0.2 and 0.3 reduces the effects of supply air and Contaminant Source. The recirculated air curtain reduces the dominant area of air supply inlet, with regional revised accessibility decreasing from 0.69 to 0.54. Air curtain at an appropriate airflow rate significantly reduces the effect of Contaminant Source at distant areas, with regional revised accessibility decreasing from 1.03 to 0.64. The proposed method is beneficial for evaluating air recirculation in establishing indoor environment.
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rapid identification of single constant Contaminant Source by considering characteristics of real sensors
Journal of Central South University, 2012Co-Authors: Xianting Li, Lingjuan Kong, Xiaoliang ShaoAbstract:For the release of hazardous Contaminant indoors, Source identification is critical for developing effective response measures. A method which can quickly and accurately identify the position, emission rate, and release time of a single constant Contaminant Source by using real sensors was presented. The method was numerically demonstrated and validated by a case study of Contaminant release in a three-dimensional office. The effects of the measurement errors and total sampling period of sensor on the performance of Source identification were thoroughly studied. The results indicate that the adverse effects of the measurement errors can be mitigated by extending the total sampling period. For reaching a desirable accuracy of Source identification, the total sampling period should exceed a certain threshold, which can be determined by repeatedly running the identification method until the results tend to be stable. The method presented can contribute to develop an onsite Source identification system for protecting occupants from indoor releases.
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evaluating emergency ventilation strategies under different Contaminant Source locations and evacuation modes by efficiency factor of Contaminant Source efcs
Building and Environment, 2010Co-Authors: Weiding Long, Xianting Li, Douglas E BarkerAbstract:Emergency ventilation plays an important role in protecting occupants when a hazardous Contaminant is released indoors. A number of studies have been conducted to better understand how to protect indoor occupants with effective ventilation strategies. However, little attention has been paid to the impact of the non-uniform and time-dependent distribution of occupants during evacuation. A new concept, Efficiency Factor of Contaminant Source (EFCS), has recently been proposed to evaluate the performance of emergency ventilation by comprehensively considering the spatial and temporal distributions of both the Contaminant and occupants. This paper aims to: (1) propose and demonstrate a procedure for determining an optimal ventilation strategy by using EFCS; (2) examine the effects of Source locations, ventilation modes, and evacuation modes on the performance of emergency ventilation. One hundred cases with ten ventilation modes, two evacuation modes, and five Source locations were investigated numerically. The results show that the EFCS concept can provide a reasonable way to evaluate the performance of emergency ventilation. The threats of different Source locations may vary over a large range, and certain measures should be taken to monitor and prevent the releases at high threat locations. A system equipped with multiple ventilation modes is necessary since no universal ventilation mode can successfully mitigate all hazardous situations. The effects of an evacuation mode may be more significant than that of a ventilation mode under certain situations.
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numerical investigation on the influence of Contaminant Source location occupant distribution and air distribution on emergency ventilation strategy
Indoor and Built Environment, 2005Co-Authors: Xi Chen, Bin Zhao, Xianting LiAbstract:The purpose of emergency ventilation is to remove pollutants effectively that may be suddenly released indoors. With the occurrence of indoor terrorism attacks and the accidental release of toxic chemicals, people have paid more attention of such emergency events in recent years. The traditional idea of a strategy for emergency ventilation is simply to supply sufficient fresh air into rooms. However, the positioning of a Contaminant Source, the distribution of occupants and the overall airflow pattern are also important factors. A new concept, integrated accessibility of Contaminant Source (IACS), has recently been proposed to consider those factors simultaneously. By using this concept as a major index of ventilation efficiency, 84 cases with different combinations of Contaminant Source and ventilation style in a full-scale room in which the occupant distribution is prescribed have been investigated numerically in this paper. The results show that air exchange rate is not the only dominant parameter, and...