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Simultaneous identification of groundwater pollution source location and release concentration using Artificial Neural Network
Environmental Forensics  (IF1.328),  Pub Date : 2020-11-27, DOI: 10.1080/15275922.2020.1850566
Jyoti Chaubey, Rajesh Srivastava

Abstract

Groundwater pollution source identification problem plays an important role in designing the groundwater remediation measures. This study aims at identifying the groundwater pollutant source location and its strength given a set of concentration breakthrough curves at different locations. Feed forward three layered artificial neural network (ANN) has been used to identify these two source parameters: location, i.e., the distance of source from the observation point and strength, i.e., the release concentration. A simplified ANN architecture is achieved by presenting the available concentration breakthrough curves in a specific manner as input to the model. The database of 1750 patterns for training and testing of the model is generated employing analytical solution of one dimensional steady flow and transient contaminant transport in a homogeneous aquifer. The performance of the ANN model was evaluated using standard statistical methods. A network with architecture 22-16-2 was found to be optimum and resulted in a normalized root mean square error (NRMSE) of 0.014 for identifying the source location and NRMSE of 0.044 for identifying the source release concentration. The results show that ANN can be a very efficient tool for locating pollution sources and estimating the release concentration at source. A good ANN model performance was obtained even with a simple architecture and with a small number of input variables.