Hydrology Journal
Year : 2006, Volume : 29, Issue : 1&2
First page : ( 55) Last page : ( 66)
Print ISSN : 0971-569X.

Neural network based solutions for locating groundwater pollution sources

Kumar Jitendra, Jain Ashu, Srivastava Rajesh

Department of Civil Engineering, Indian Institute of Technology, Kanpur-208016.

Abstract

The identification of pollution sources in aquifers is an important area of research for hydrologists and governmental agencies. It may be possible to locate the polluting industry, given the data in terms of pollutant concentration measurements at observation wells and the aquifer parameters. Traditionally, hydrologists have relied on the conceptual methods for the identification of groundwater pollution sources. Recently, artificial neural networks (ANNs) have emerged as an attractive and easy to implement alternative to solve complex problems efficiently. Some researchers have used ANNs for the identification of pollution sources in aquifers. A major problem with most previous studies using ANNs has been the large size of the neural networks that are needed to model the inverse problem. The breakthrough curves at an observation well may consist of hundreds of concentration measurements, and presenting all of them to the input layer of an ANN not only results in huge networks hut also requires large amount of training and testmg data sets to develop the ANN models. In this paper, we present the results of a study aimed at estimating groundwater pollution source location using ANNs through the use of two different methods of presenting the breakthrough curves data as inputs to the ANN models. To simplify the ANN architectures, these methods do not employ the whole breakthrough curves as the inputs to the ANNs. The feed-forward multi-layer perceptron type of ANN architecture was employed to develop various ANN models which were trained using the back-propagation method. The results show that the ANNs can be very efficient tools for locating pollution sources and that it is possible to obtain good ANN model performance even with extremely simplified architectures involving a very few input variables.

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Key words

pollution identification, aquifers, ANN.

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