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 JWARP  Vol.8 No.3 , March 2016
Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model
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.
Cite this paper: Leichombam, S. and Bhattacharjya, R. (2016) Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model. Journal of Water Resource and Protection, 8, 411-424. doi: 10.4236/jwarp.2016.83034.
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