Saturday, August 27, 2011

Detecting Groundwater Pollution Source Through Simulated Evolution

I have not yet seen an application of evolutionary computation like this one: Tracking groundwater pollution to its source. But it seems they also apply other kinds of soft computing (neural networks and simulated annealing).


They point out that reliable and accurate estimation of unknown groundwater pollution sources remains a challenge because of the uncertainties involved and the lack of adequate observation data in most cases. The non-unique nature of the identification results is also an issue in finding the original source of a pollutant. They have tested the validity of different optimization algorithms including a genetic algorithm, an artificial neural network and simulated annealing and hybrid methods. All of these methods essentially process available data including pollutant concentrations and how these change over time and any monitoring data to home in on a potential source. The benefit of using such algorithms is that as more information becomes available another iteration will take investigators closer to the source.

It seems an interesting application of artificial intelligence.

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