Abstract
The linked simulation-optimization model can be used for solving a complex groundwater pollution source identification problem. Advanced simulators have been developed and successfully linked with numerous optimization algorithms for identification of groundwater pollution sources. However, the identification of pollution sources in a groundwater aquifer using linked simulation-optimization model has proven to be computationally expensive. To overcome this computational burden, an approximate simulator, the artificial neural network (ANN) model can be used as a surrogate model to replace the complex time-consuming numerical simulation model. However, for large-scale aquifer system, the performance of the ANN-based surrogate model is not satisfactory when a single ANN model is used to predict the concentration at different observation locations. In such a situation, the model efficiency can be enhanced by developing separate ANN model for each of the observation locations. The number of ANN models is equal to the number of observation wells in the aquifer. As a result, the complexity of the ANN-based simulation-optimization model will be related to the number of observation wells. Thus, this study used a modified formulation to find out the optimal numbers of observation wells which will eventually reduce the computational time of the model. The performance of the ANN-based simulation-optimization model is evaluated by identifying the groundwater pollutant sources of a hypothetical study area. The limited evaluation shows that the model has the potential for field application.
Highlights
Identifying the groundwater pollutant source is a very difficult and time-consuming process due to the involvement of aquifer simulation model with the optimization model
The artificial neural network (ANN) models developed for the thirty observation wells of the illustrative study area are incorporated with the optimization model for identification of pollutant sources and the optimal location of the monitoring wells
For reducing the computational time of the groundwater flow and transport processes, ANN model has been used as an approximate simulator
Summary
Identifying the groundwater pollutant source is a very difficult and time-consuming process due to the involvement of aquifer simulation model with the optimization model. They used the least square method and linear programming for solving the optimization model This approach was adopted by many researchers in the earlier stage of identification of unknown groundwater pollutant sources [2]-[7]. [15] used Genetic Algorithm for solving the multiple unknown pollutant sources They linked the optimization model to an external flow and transport simulation model and eliminated the computational complexity in solving large embedded non-linear optimization models. Artificial neural network (ANN) model is one of the most effective as well as popular models used for replacing the numerical aquifer simulation model This has been confirmed by various groundwater management studies that introduction of ANN enhances the computational efficiency of the linked simulation-optimization model. The performance of the developed methodology is evaluated in a large hypothetical study area by identifying groundwater pollutant sources
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
