American Society of Agricultural and Biological Engineers

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Towards An Optimal Integrated Space-Time Design for Water Quality Monitoring Networks

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  013068,  2001 ASAE Annual Meeting. (doi: 10.13031/2013.7420) @2001
Authors:   Farqad AlKhal; Hassan Ghaziri, Rabi H. Mohtar
Keywords:   Non-linear optimization, plume characterization, stochastic flow fields, test well design, reduced-gradient search

This paper presents an approach for designing cost-effective water quality monitoring networks for monitoring and detection of groundwater contamination. The proposed approach aimed at reducing the number and frequency of wells sampled while maintaining high accuracy of characterizing contaminant plumes. The approach used in this paper is optimization-based so that monitoring network design is formulated mathematically as an optimization problem where the decision is made on where and when to construct monitoring wells and when to sample the constructed wells so as to minimize the total cost of well construction and sampling while maintaining a prescribed level of accuracy. With the aid of a contaminant transport model, which can help determine the characterization error associated with any given design, reduced-gradient search techniques were utilized to solve the resulting complex non-linear optimization problem. This optimization module is hoped to form a basic building block in an adaptive design approach that is dynamic in nature so that a preliminary network design can be revised periodically as new groundwater quality information is collected with the construction and sampling of new wells. The methodology was tested using a contaminant plume distribution and the sensitivity of the output solution to the choice of initial solution, the plume characterization error accuracy level, and the allowed sampling time periods were conducted and are presented. Testing of other sites and the effect of spatial variability on the final design along with other optimization techniques targeted at non-convex, non-linear problems like simulated annealing are being tested.

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