Optimal monitoring network design for efficient identification of unknown groundwater pollution sources

Prakash, Om, and Datta, Bithin (2014) Optimal monitoring network design for efficient identification of unknown groundwater pollution sources. International Journal of Geomate, 6 (1). pp. 785-790.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://www.gi-j.com/vol6no1serial11.htm
 
12
5


Abstract

Application of linked simulation-optimization approach for solving groundwater identification problems is well established. Pollutant concentration measurements from different sets of monitoring locations, when used in a linked simulation-optimization approach, results in different degrees of accuracy of source identification. Moreover, the accuracy of source identification results depends on the number and spatiotemporal locations of pollutant concentrations measurements. This study aims at improving the accuracy of source identification results, by using concentration measurements from an optimally designed monitoring network. A linked simulation optimization based methodology is used for optimal source identification. Genetic programming based impact factor is used for designing the optimal monitoring network. Concentration measurement data from the designed network is then used, in the Simulated Annealing based linked simulation-optimization model for efficient source identification. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show improvement in the efficiency in source identification when such designed monitoring networks are utilized.

Item ID: 32200
Item Type: Article (Research - C1)
ISSN: 2186-2990
Keywords: optimal monitoring network; groundwater pollution; genetic programming; multi-objective optimization; pollution source identification; simulated annealing
Additional Information:

Freely available from publisher website.

Funders: CRC CARE, James Cook University (JCU)
Date Deposited: 12 Jun 2014 05:09
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
09 ENGINEERING > 0907 Environmental Engineering > 090799 Environmental Engineering not elsewhere classified @ 50%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%
Downloads: Total: 5
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page