Application of Data-Driven and Optimization Methods in Identification of Location and Quantity of Pollutants
Publication: Journal of Hazardous, Toxic, and Radioactive Waste
Volume 19, Issue 2
Abstract
Water pollution is one of the major problems in providing and preserving water resources, so identifying the pollution source plays a critical role in regulation actions. Thus, this paper addresses the process of pollution source identification, including location, concentration, and the time of injection in surface water by using a data-mining method [artificial neural network (ANN)] and optimization techniques [genetic algorithm (GA) and pattern search (PS)]. The CE-QUAL-W2 numerical model is used to produce input and output data in ANN and simulation models. To check the capability of the methodology, the identification of various hypothetical examples of pollution with several forms of injection of the pollutant in a nonprismatic water canal is performed. Results of data-driven and optimization methods are evaluated by employing statistical criteria. Final results show that the ANN method is capable of identifying a pollutant injection hydrograph and it is relatively sensitive to the accuracy of monitoring so that like the optimization method for errorless data, the determination coefficient () is nearly 100%, and the absolute average relative error (AARE) and total error () are nearly zero. If there is more than one active pollutant injection source and evaluation of the pollutant concentration is accompanied with errors, the optimization method gives better results than ANN so that in pollutant sources and for errors of 5 and 10%, the statistic AARE in the optimization method is less than 3.7 and 5.7% while in ANN it is less than 10.1 and 14.3%. If not all of the pollutant sources are active, the optimization method can well identify the inactive source even in several levels of error, while ANN can only identify the inactive source if there is no error.
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Journal of Hazardous, Toxic, and Radioactive Waste
Volume 19 • Issue 2 • April 2015
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© 2014 American Society of Civil Engineers.
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Received: Feb 21, 2014
Accepted: Jun 6, 2014
Published online: Jul 9, 2014
Discussion open until: Dec 9, 2014
Published in print: Apr 1, 2015
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