Estimating groundwater pollution source location from observed breakthrough curves using neural networks

Jitendra Kumar, Ashu Jain, Rajesh Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents the results of a study aimed at estimating groundwater pollution source location from observed breakthrough curves using neural networks. Two different methods of presenting the breakthrough curves to the ANN are investigated. The feed-forward multi-layer perceptron (MLP) type artificial neural network (ANN) models are employed. The ANNs were trained using the back-propagation training algorithm on simulated data. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training or under-training of the ANNs. The preliminary results indicate that the ANNs are very efficient tools for estimating the distance of the potential pollution source from the observation well where breakthrough curve is measured.

Original languageEnglish
Title of host publicationProceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
Pages1004-1017
Number of pages14
StatePublished - 2005
Externally publishedYes
Event2nd Indian International Conference on Artificial Intelligence, IICAI 2005 - Pune, India
Duration: Dec 20 2005Dec 22 2005

Publication series

NameProceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005

Conference

Conference2nd Indian International Conference on Artificial Intelligence, IICAI 2005
Country/TerritoryIndia
CityPune
Period12/20/0512/22/05

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