Abstract
Groundwater contamination is serious threat to health of human being and environment. It is difficult and expensive to remediate the polluted aquifers. Identification of unknown pollution sources is first step towards adopting any remediation strategy. The proposed methodology characterizes concentration breakthrough curves in terms of statistical parameter such as average value, maximum value, standard deviation, skewness and kurtosis. The characterized parameters are utilized in a feed forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes and duration of activity. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentations are performed with different number of training and testing patterns.
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Singh, R.M., Srivastava, D. (2013). Groundwater System Modeling for Pollution Source Identification Using Artificial Neural Network. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_20
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DOI: https://doi.org/10.1007/978-3-319-03756-1_20
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