Groundwater Monitoring Network Optimization with Redundancy Reduction

Abstract

Three optimization models are proposed to select the best subset of stations from a large groundwater monitoring network: ͑1͒ one that maximizes spatial accuracy; ͑2͒ one that minimizes temporal redundancy; and ͑3͒ a model that both maximizes spatial accuracy and minimizes temporal redundancy. The proposed optimization models are solved with simulated annealing, along with an algorithm parametrization using statistical entropy. A synthetic case-study with 32 stations is used to compare results of the proposed models when a subset of 17 stations are to be chosen. The first model tends to distribute the stations evenly in space; the second model clusters stations in areas of higher temporal variability; and results of the third model provide a compromise between the first two, i.e., spatial distributions that are less regular in space, but also less clustered. The inclusion of both temporal and spatial information in the optimization model, as embodied in the third model, contributes to selection of the most relevant stations.

Optimal Grondwater Monitoring Network Design for Pollution Plume Estimation With Active Sources

“…At high temperature atoms with high energy move freely and when the temperature is reduced get ordered and finally form crystals having minimum possible energy. The SA parameterstemperature reduction factor, initial temperature, number of function evaluations for termination criteria are based on the sensitivity analysis as well as guidelines available in the literature [13] and [8].…”

“…The method of simulated annealing is a more flexible and generally applicable heuristic optimization technique and efficient in locating global optimal solutions. This method of optimization has been used successfully for large scale applications in groundwater [8]. This optimization algorithm is chosen for its efficiency in achieving a global optimal solution.…”

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

“…Applications to groundwater quality monitoring networks, stream gauge networks, and water distribution networks have increased in recent years. The methods used in network research related to entropy include least square methods and entropy [4], kriging [5], information entropy [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and combined kriging and information entropy [23][24][25]. In particular, the information entropy approach has been widely adopted since the 1970s for hydrologic data collection network design and uncertainty evaluation [26][27][28][29][30][31][32][33].…”

Stochastic search algorithms for optimal design of monitoring networks

“…In particular, several authors have developed optimal network design with the SA algorithm (Ferri and Piccioni, 1992;Pardo-Igúzquiza, 1998;Banjevic and Switzer, 2001;Ferreyra et al, 2002;Nunes et al, 2004;Fuentes et al, 2007) and the GA (Reed et al, 2000a;Wu et al, 2005). The SA is able to obtain locally optimal solutions, but very often gets trapped in regions of the design space far from the global optimum.…”