OPTIMAL MONITORING NETWORK DESIGN FOR EFFICIENT IDENTIFICATION OF UNKNOWN GROUNDWATER POLLUTION SOURCES

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.