Simulation–Optimization-Based Virus Source Identification Model for 3D Unconfined Aquifer Considering Source Locations and Number as Variable

Abstract

Identification of virus sources is one of the most important activities to control the spread of epidemics. Virus sources can be identified using an inverse optimization model. The inverse optimization model minimizes the error between simulated and observed concentrations at observation locations of the aquifer. The observed concentration can be obtained from field observation of contaminants. The simulated concentration can be obtained through the flow and virus transport simulation models. As such, the flow and transport simulation models need to be incorporated into the optimization model. As a result, the complexity of the problem is related to the dimension of the simulation models. For reducing the computational burden, generally, one- or two-dimensional simulation model is considered in finding the virus sources. However, to mimic the real world situation, one has to use the three-dimensional (3D) virus transport processes. Furthermore, in earlier studies, the number and the source locations are considered to be known. Thus, this study deals with the identification of a virus source in an unconfined 3D groundwater aquifer considering source location and number as variables. The methodology proposed allows running the models in an external environment to generate the simulated concentrations with arbitrary sources. The optimization model is solved using the pattern search algorithm. Two hypothetical problems have been considered to show the potential of the algorithm. The promising results show that virus sources in an aquifer can be identified even when the location and number of sources are not known.