Bithin Datta

Identification of Pollution Sources in Transient Groundwater Systems

A methodology using a nonlinear optimization model is presentedfor estimating unknown magnitude, location and duration ofgroundwater pollution sources under transient flow and transportconditions. The proposed optimization model incorporates thegoverning equations of flow and solute transport as binding equalityconstraints, and thus essentially simulates the physical processes oftransient flow and transient transport in the groundwater systems.The proposed inverse model identifies unknown sources of pollution byusing measured values of pollutant concentration at selectedlocations. Performance of the proposed model for the identificationof unknown groundwater pollution sources is evaluated for anillustrative study area in a hypothetical confined aquifer underdifferent cases of data availability. The effect of observation welllocation vis-à-vis pollution source location on identificationaccuracy is also investigated. Performance of the developedidentification model is also evaluated for a condition whenconcentration measurements are missing during few initial timeperiods after the pollution sources become active. The effect ofspecified initial guesses of the variable values on the optimalsolutions are also investigated. These performance evaluation resultsdemonstrate the limitations and potential applicability of theproposed optimization model for identifying the sources of pollutionin transient groundwater systems.

Saltwater intrusion management of coastal aquifers I: linked simulation-optimization

A set of different methodologies are developed for multiple objective management of coastal aquifers. The coastal aquifer management models are developed using a numerical simulation model, meta-model, and the multiple objective optimization algorithm NSGA-II. The NSGA-II algorithm is also modified to accommodate initial solution generation using the Latin hypercube sampling for uniform sampling in bound space. These initial solutions are useful to improve the efficiency of the optimization algorithm. One important issue in developing management models for coastal aquifers incorporating the density dependent flow and transport processes is the computational feasibility. A few variations of the management model are also evaluated to test the potential for improving the computational efficiency. The variations in the formulation of management models include: direct linking of numerical simulation model, introducing meta-models [in this study artificial neural network (ANN)] in place of original numerical si...

Coupled simulation‐optimization model for coastal aquifer management using genetic programming‐based ensemble surrogate models and multiple‐realization optimization

Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden.

Application of optimisation techniques in groundwater quantity and quality management

This paper presents the state-of-the-art on application of optimisation techniques in groundwater quality and quantity management. In order to solve optimisation-based groundwater management models, researchers have used various mathematical programming techniques such as linear programming (LP), nonlinear programming (NLP), mixed-integer programming (MIP), optimal control theory-based mathematical programming, differential dynamic programming (DDP), stochastic programming (SP), combinatorial optimisation (CO), and multiple objective programming for multipurpose management. Studies reported in the literature on the application of these methods are reviewed in this paper.

Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing

Determination of groundwater contaminant source characteristics such as release histories of unknown groundwater pollutant sources from concentration observation data is an inverse problem. Often solution to this inverse problem is nonunique, and it is an ill-posed problem. A linked simulation-optimization approach can be used to solve this problem efficiently. However, this approach is computationally intensive, and the results obtained tend to be highly susceptible to errors in the measured data and estimated hydrogeological parameters. Apart from this, accuracy of the solutions is highly dependent on the choice of monitoring locations. An adaptive simulated annealing (ASA)-based solution algorithm is shown to be computationally efficient for optimal identification of the source characteristics in terms of execution time and accuracy. This computational efficiency appears to prevail even with moderate levels of errors in estimated parameters and concentration measurement errors. Also, the contaminant concentration monitoring locations are shown to be critical in the efficient characterization of the unknown contaminant sources. Optimal identification results for different monitoring networks are presented to demonstrate the relevance of a network suitable for efficient source identification.

Groundwater Pollution Source Identification and Simultaneous Parameter Estimation Using Pattern Matching by Artificial Neural Network

Pollution of groundwater often occurs because of unknown disposal of toxic wastes, especially from industrial sites, or due to undetected leakage from pipes, waste storage containers, or underground tanks. The determination of pollution sources by using only concentration measurement data in the aquifer is analogous to reconstructing the history of events that has occurred in the aquifer over a time horizon. Identification of unknown groundwater pollution sources becomes more difficult when the hydrogeologic flow and transport parameters are also unknown. A trained artificial neural network (ANN) can be utilized to simultaneously solve the problems of estimating unknown groundwater pollution sources and estimating unknown hydrogeologic parameters (hydraulic conductivity, porosity, and dispersivities). In this article, the universal function approximation property of a multilayer, feed-forward ANN was utilized to estimate temporally and spatially varying unknown pollution sources, as well as to provide a reliable estimation of unknown flow and transport parameters. ANN was trained on patterns of simulated data using a back-propagation algorithm. A set of source fluxes and temporally varying simulated concentration measurements constituted the pattern for training. This article also describes a potential applicability of this methodology by using an illustrative example. Additionally, the methodology performance is evaluated under varying concentration measurement errors. The limited performance evaluations show that the proposed methodology performs reasonably well even with large measurement errors.

Interactive computer graphics-based multiobjective decision-making for regional groundwater management

This paper presents a comprehensive set of procedures by which decision-makers can select a single strategy from a set of alternative strategies. Application of these procedures in developing a regional conjunctive water management strategy for an important rice producing area in Arkansas, U.S.A., is described. The importance of interactive decision-making and efficient presentation of information through interactive graphics and computations is also demonstrated. The optimization model considers two different objectives: minimization of the total cost of water use (including opportunity cost due to the loss in agricultural production caused by non-availability of water); and maximization of total withdrawal (pumping) from the aquifer. Application of the surrogate worth tradeoff method together with interactive computer graphics display of relevant information is presented as part of the procedures for selecting a regional sustained groundwater withdrawal strategy.

Review: Simulation-optimization models for the management and monitoring of coastal aquifers

The literature on the application of simulation-optimization approaches for management and monitoring of coastal aquifers is reviewed. Both sharp- and dispersive-interface modeling approaches have been applied in conjunction with optimization algorithms in the past to develop management solutions for saltwater intrusion. Simulation-optimization models based on sharp-interface approximation are often based on the Ghyben-Herzberg relationship and provide an efficient framework for preliminary designs of saltwater-intrusion management schemes. Models based on dispersive-interface numerical models have wider applicability but are challenged by the computational burden involved when applied in the simulation-optimization framework. The use of surrogate models to substitute the physically based model during optimization has been found to be successful in many cases. Scalability is still a challenge for the surrogate modeling approach as the computational advantage accrued is traded-off with the training time required for the surrogate models as the problem size increases. Few studies have attempted to solve stochastic coastal-aquifer management problems considering model prediction uncertainty. Approaches that have been reported in the wider groundwater management literature need to be extended and adapted to address the challenges posed by the stochastic coastal-aquifer management problem. Similarly, while abundant literature is available on simulation-optimization methods for the optimal design of groundwater monitoring networks, applications targeting coastal aquifer systems are rare. Methods to optimize compliance monitoring strategies for coastal aquifers need to be developed considering the importance of monitoring feedback information in improving the management strategies.RésuméLa littérature sur l’application des approches de simulation et d’optimisation pour la gestion et le suivi des aquifères côtiers est passée en revue. Les approches de modélisation de l’interface aussi bien nette que dispersive sont appliquées conjointement aux algorithmes d’optimisation dans le passé pour développer des solutions de gestion pour les intrusions d’eau salée. Les modèles de simulation et d’optimisation basés sur une approximation de l’existence d’une interface nette sont souvent basés sur la relation de Ghyben-Herzberg et fournissent un cadre efficace pour définir de manière préliminaire des schémas de gestion de l’intrusion saline. Les approches reposant sur des modèles numériques prenant en considération une interface dispersive ont des applications plus variées mais sont mis au défi par la charge de calcul induite lorsqu’elles sont appliquées dans un cadre de simulation et d’optimisation. L’utilisation de modèles de substitution pour remplacer le modèle physique lors de l’optimisation obtient des succès dans de nombreux cas. La question du changement d’échelle est toujours un défi pour l’approche de modélisation de substitution du fait que l’avantage du calcul numérique est associé au temps de formation nécessaire pour les modèles de substitution, croissant avec la taille du problème. Peu d’études ont tenté de résoudre les problèmes de gestion des aquifères côtiers de manière stochastique en considérant la prévision de l’incertitude. Les approches qui sont rapportées dans la littérature relative à la gestion des eaux souterraines doivent être étendues et adaptées pour répondre au défis posés par le problème de gestion des aquifères côtiers par approche stochastique. De manière similaire, alors qu’une littérature abondante est disponible concernant les méthodes de simulation et d’optimisation pour la conception optimale de réseaux de suivi des eaux souterraines, des applications ciblant les aquifères côtiers sont rares. Les méthodes pour optimiser la mise en œuvre des stratégies de suivi des aquifères côtiers nécessitent d’être développées considérant l’importance des données issues des suivis pour améliorer les stratégies de gestion.ResumenSe realiza una revisión de la bibliografía sobre la aplicación de los enfoques de simulación y optimización para la gestión y seguimiento de los acuíferos costeros. En el pasado se aplicaron ambos enfoques de modelación para la definición y dispersión de la interfaz en conjunto con algoritmos de optimización para el desarrollo de soluciones de gestión en la intrusión de agua salada. Los modelos de simulación y de optimización a menudo están basados en la relación de Ghyben-Herzberg y proporcionan un marco eficiente para diseños preliminares de los planes de gestión de la intrusión de agua salada. Los modelos basados en modelos numéricos de interfaz dispersiva tienen una aplicabilidad más amplia, pero presentan un desafío por la carga computacional involucrada cuando se aplica en el marco de simulación y de optimización. Se encontró que el uso de modelos sustitutos para reemplazar el modelo de base física durante la optimización puede ser eficaz en muchos casos. La escalabilidad es todavía un desafío para el enfoque de la modelación sustituta como una ventaja computacional acumulada que se compensa con el tiempo de entrenamiento requerido para los modelos sustitutos al aumentar el tamaño del problema. Pocos estudios han intentado resolver los problemas de gestión costera de acuíferos considerando modelos estocásticos de predicción de la incertidumbre. Los enfoques que se han reportado en la literatura de gestión de las aguas subterráneas en general deben ampliarse y adaptarse para hacer frente a los retos que plantea el problema estocástico de la gestión costera de acuíferos. Del mismo modo, mientras está disponible una abundante literatura sobre los métodos de simulación y de optimización para el diseño óptimo de redes de monitoreo de las aguas subterráneas, las aplicaciones destinadas a los sistemas acuíferos costeros son poco frecuentes. Los métodos para optimizar las estrategias de control del cumplimiento en los acuíferos costeros deben desarrollarse teniendo en cuenta la importancia de monitorear la información de retorno para mejorar de las estrategias de gestión.摘要本文论述了沿海含水层管理和监测模拟-最优化方法应用方面的文献。过去应用锋利界面和分散界面模拟方法,结合最优化算法开发了海水入侵的管理解决方案。基于锋利界面近似法的模拟-最优化模型通常以Ghyben-Herzberg的相互关系为基础,为海水入侵管理方案的初步设计提供了有效率的框架。基于分散-界面数值模型的模型具有更广的适用性,但在应用在模拟-最优化框架中受到了有关计算负担的挑战。发现在最优化期间使用代用模型替代物理模型在很多情况下很成功。随着计算题规模大小的增加,应计的计算优势与代用模型所需的培训时间交换,可扩展性对于代用模拟方法仍然是一个挑战。考虑到模型预测的不确定性,极少的研究试图解决随机的沿海含水层管理问题。众多地下水管理文献中记载的方法需要扩充和完善,以注重随机的沿海含水层管理问题提出的挑战。同样,假如地下水监测网络最优设计模拟-最优化模型方面有丰富的文献,针对沿海含水层系统的应用就很少。考虑到改善管理战略中监测反馈信息的重要性,需要开发最优化沿海含水层适合的监测战略的方法。ResumoA literatura sobre a aplicação de abordagens de otimização-simulação para o gerenciamento e o monitoramento de aquíferos costeiros é revisada. No passado, ambas abordagens de modelagem, de interface abrupta e dispersiva, foram adotadas em conjunto com algoritmos de otimização para desenvolver soluções de gerenciamento para a intrusão de água salina. Os modelos de otimização-simulação baseados na aproximação de interface abrupta são frequentemente baseados na relação Ghyben-Herzberg e fornecem uma estrutura eficiente para designs preliminares de esquemas de gerenciamento da intrusão de água salina. Modelos baseados em modelos numéricos de interface dispersiva possuem aplicabilidade mais ampla, porém são colocados a prova pela carga computacional envolvida quando aplicados na estrutura de otimização-simulação. A utilização de modelos substitutos para substituir o modelo fisicamente embasado durante a otimização obteve sucesso em muitos casos. A expansividade se mantem um desafio para a abordagem de modelagem substituta, assim como a vantagem computacional ampliada é escolhida de acordo com o tempo de treinamento requisitado para os modelos substitutos e com o aumento do tamanho do problema. Alguns estudos tentaram resolver problemas estocásticos de gerenciamento de aquíferos costeiros considerando a incerteza predita pelo modelo. Abordagens que têm sido amplamente relatadas na literatura sobre o gerenciamento de água subterrânea precisam ser expandidas e adaptadas para dirigirem-se aos desafios colocados pelo problema de gerenciamento estocástico de aquíferos costeiros. Similarmente, enquanto está disponível uma literatura abundante em métodos de otimização-simulação para o design ótimo de redes de monitoramento de água subterrânea, aplicações mirando aquíferos costeiros são raras. Métodos que otimizem estratégias de monitoramento de conformidades para aquíferos costeiros precisam ser desenvolvidos considerando a importância do retorno das informações do monitoramento na melhoria de estratégias de gerenciamento.

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.

Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.