An optimization-based methodology for identifying unknown sources of ground-water pollution is presented. The proposed methodology utilizes an optimization model in which the flow and transport equations are embedded as constraints. A nonlinear programming algorithm is used to obtain as solution the optimal estimates of unknown source characteristics. The input to this model includes measured pollutant concentration at observation sites. The source identification methodology is further extended to the simultaneous estimation of aquifer parameters as well as identification of unknown pollutant sources. Performance of the developed methodology is evaluated for illustrative examples considering two-dimensional flow and advective-dispersive solute transport. Different cases including variability in data availability, single and multiple potential source locations, and errors in measurement data are considered. These performance evaluations demonstrate that at least for the illustrative example problems, the proposed methodology performs satisfactorily in identifying the locations, determining the magnitudes, and specifying duration of the unknown ground-water pollution sources, even when the aquifer parameters are unknown.

The genetic algorithm (GA)–based simulation optimization approach is used for optimal identification of unknown groundwater pollution sources. Simple as well as complex scenarios of multiple unknown groundwater pollution sources are considered. A flow and transport simulation model is externally linked to the GA-based optimization model to simulate the physical processes involved. The simulation model uses potential pollution source characteristics that are evolved by the GA and simulates the resulting concentration measurement values at observation locations. These simulated spatial and temporal pollutant concentration measurement values are used to evaluate the fitness function value of the GA. The main advantage of the proposed methodology is the external linking of the numerical simulation model with the optimization model. This approach makes it feasible to solve the source-identification problems for complex aquifer study areas with multiple unknown pollution sources. The performance of the develope...

A methodology combining an optimal ground-water–quality monitoring network design and an optimal source-identification model is presented. In the first step of the three-step methodology, an embedded nonlinear optimization model is utilized for preliminary identification of pollutant sources (magnitude, location, and duration of activity) based on observed concentration data from arbitrarily located existing wells. The second step utilizes these preliminary identification results and a simulation optimization approach to design an optimal monitoring network that can be implemented in the subsequent time periods. In the third step, the observed concentration data at the designed monitoring well locations are utilized for more accurate identification of the pollutant sources. The design of the monitoring network can be dynamic in nature, with sequential installation of monitoring wells during subsequent time periods. The monitoring network can be implemented in stages, in order to utilize the updated inform...

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...