Groundwater Pollution Source Identification and Simultaneous Parameter Estimation Using Pattern Matching by Artificial Neural Network

Abstract

Pollution of groundwater often occurs because of unknown disposal of toxic wastes, especially from industrial sites, or due to undetected leakage from pipes, waste storage containers, or underground tanks. The determination of pollution sources by using only concentration measurement data in the aquifer is analogous to reconstructing the history of events that has occurred in the aquifer over a time horizon. Identification of unknown groundwater pollution sources becomes more difficult when the hydrogeologic flow and transport parameters are also unknown. A trained artificial neural network (ANN) can be utilized to simultaneously solve the problems of estimating unknown groundwater pollution sources and estimating unknown hydrogeologic parameters (hydraulic conductivity, porosity, and dispersivities). In this article, the universal function approximation property of a multilayer, feed-forward ANN was utilized to estimate temporally and spatially varying unknown pollution sources, as well as to provide a reliable estimation of unknown flow and transport parameters. ANN was trained on patterns of simulated data using a back-propagation algorithm. A set of source fluxes and temporally varying simulated concentration measurements constituted the pattern for training. This article also describes a potential applicability of this methodology by using an illustrative example. Additionally, the methodology performance is evaluated under varying concentration measurement errors. The limited performance evaluations show that the proposed methodology performs reasonably well even with large measurement errors.