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The present study deals with the analysis of virus transport in groundwater and estimation of transport parameters. The parameter estimation is formulated as a least-squares minimization problem in which the parameters are estimated by minimizing the deviations between the model predicted and experimentally observed virus concentrations. A parameter estimation procedure is developed by coupling a hybrid finite volume numerical model simulating one-dimensional virus transport with the Levenberg-Marquadart algorithm. The numerical model employs a globally second order accurate explicit finite volume method for the advective transport and an implicit finite difference method for the dispersive transport. The comparison of model prediction with analytical solution shows that the numerical model accurately predicts virus movement in both advective and dispersive dominated transport. The parameter estimation indicates that for the case of estimating more than three unknown parameters, the inverse procedure results in nonunique optimal estimates. Further, for the case of estimating two or three unknown parameters, the presence of inactivation coefficients of liquid and sorbed phases also results in nonunique estimates. It is concluded that a priori estimation of one of the inactivation coefficient is necessary for unique estimation of other unknown parameters. A detailed statistical analysis is carried out to study the effect of bias induced by the objective function on the estimated parameters when the data contains the errors. It is found that in the presence of noise in the virus concentration data, the objective function does not induce any bias on the parameter estimates as the true value falls within 95% confidence levels at all noise levels. [ABSTRACT FROM AUTHOR]

An optimal groundwater remediation design problem generally requires determination of the location of extraction and injection wells and their pumping and injection rates once the well locations are selected. According to many researchers, the determination of an optimal well location is even more important than the optimal pumping rate in groundwater remediation problems. The objective of the study is to apply a neural network embedded Monte Carlo approach to determine potential well locations for in situ bioremediation of contaminated groundwater. The methodology developed in this study has three important components: a bioremediation simulation model, an artificial neural network, and an application of Monte Carlo simulations. This method has been further applied to an in situ bioremediation problem, for which data are adopted from the available literature. The results show that the proposed approach can successfully identify potential well locations from a set of preselected well locations, which can be further used in optimization algorithms to identify optimal well locations and the corresponding pumping rates. The advantage of this approach is that it reduces the size of the problem considerably by eliminating redundant well locations and hence the computational burden involved. Furthermore, the computational burden of Monte Carlo simulations is managed within a practical time frame by replacing the bioremediation simulation model with a trained neural network. [ABSTRACT FROM AUTHOR]

Given the increase in demands on water resources, optimal management policy has been sought for minimum resource utilities and maximum environmental benefits. Since the water resource system is complicated and has many uncertainties, artificial intelligence techniques, like fuzzy set theory or a genetic algorithm (GA), enable us to identify optimal decision variables more efficiently and incorporate uncertainty considerations. In this study, multiobjective programming was developed for a realistic application in Kinmen Island, Taiwan, against the scarce water resource. Three alternatives, including a desalination facility, additional activated carbon equipment for the current wastewater treatment plant (WTP), and constructed wetlands were designed to improve water quality and to ensure sufficient water supply. The GA technology is employed to search for the best solutions from the three alternatives. In addition, the fuzzy goal of cost and water quantity represented is utilized to determine the optimal control policy by the degree of satisfaction. In the optimal management of this case study, the desalination facility receives 17.8% of the treatment water, and 20.7 and 61.5% of the treatment water allocated to the current WTP and constructed wetland, respectively. The GA and fuzzy multiobjective programming are proven to facilitate the determination of the optimal solution for Kinmen’s water resource management. [ABSTRACT FROM AUTHOR]