2009 journal article

Contaminant Source Identification in Water Distribution Networks Under Conditions of Demand Uncertainty

ENVIRONMENTAL FORENSICS, 10(3), 253–263.

By: P. Vankayala n, A. Sankarasubramanian n, S. Ranjithan n & G. Mahinthakumar n

author keywords: contaminant source identification; water distribution system; noisy genetic algorithms; uncertainty; optimization simulation
TL;DR: Results show that noisy GA is more robust and is less computationally expensive than stochastic GA in solving the source identification problem and the autoregressive demand uncertainty model better represents the uncertainty in the source Identification process than the Gaussian model. (via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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