OPTIMAL MONITORING NETWORK AND GROUND-WATER-POLLUTION SOURCE IDENTIFICATION

A methodology combining an optimal ground-water-quality monitoring net work design and an optimal source-identification model is presented. I n the first step of the three-step methodology, an embedded nonlinear optimization modal is utilized for preliminary identification of pollu tant sources (magnitude, location, and duration of activity) based on observed concentration data from arbitrarily located existing wells. T he second step utilizes these preliminary identification results and a simulation optimization approach to design an optimal monitoring netw ork that can be implemented in the subsequent time periods. In the thi rd step, the observed concentration data at the designed monitoring we ll locations are utilized for more accurate identification of the poll utant sources. The design of the monitoring network can be dynamic in nature, with sequential installation of monitoring wells during subseq uent time periods. The monitoring network can be implemented in stages , in order to utilize the updated information in the form of observed concentration data from a time-varying (dynamic) network. The performa nce evaluation of the proposed methodology demonstrates the potential applicability of this methodology and shows significant improvement in the identification of unknown ground-water-pollution sources with lim ited observation data.