Stochastic search algorithms for optimal design of monitoring networks

Ruiz-Cárdenas, Ramiro; Ferreira, Marco A. R.; Schmidt, Alexandra M.

doi:10.1002/env.989

Cited by 32 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This optimization methodology is used, for example, in problems of rationalization of environmental monitoring network stations (Ruiz-Cárdenas et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

2014

View full text Add to dashboard Cite

The spatial variability of structures in regionalized variables are defined with the aid of geostatistical techniques, which facilitate the estimation of values for these variables in unsampled localizations and generate thematic maps to be used in decision making for localized treatments in the area under study. The quality of these maps depends on the trustworthiness of these estimates that can be modified with the choice for the sample design. The objective of this work was to establish an optimal size and shape of the sample designs in order to enhance the efficiency of sampling plans for the prediction of space dependent variables. These designs were obtained with the use of a stochastic search method called Simulated Annealing. This method is based on a sampling grid with a large number of points. Here, it is initially used to consider simulated data sets with distinct spatial dependence structures and is then used to consider real data on soy productivity. The simulated results are used as reference for the achievement of the best sample design with the lowest number of sample points that can efficiently represent the spatial dependence structure of soy productivity in a commercial area harvested by the harvester monitor. The results reported for the simulations and soy productivity data show that the optimization process was efficient in determining sample designs with reduced size, especially when using the Global Accuracy as the measurement to be maximized.

show abstract

“…This optimization methodology is used, for example, in problems of rationalization of environmental monitoring network stations (Ruiz-Cárdenas et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…However, metaheuristic strategies are used in artificial intelligence, such as the simulated annealing algorithm (Costa Filho et al, 2010;Guedes et al, 2011) and the genetic algorithm (Chakrapani and Rajan, 2008;Ruiz-Cárdenas, 2010, Guedes et al, 2011, which use an iterative search method to determine the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

2014

View full text Add to dashboard Cite

show abstract

“…Thus, the question of how to best sample airborne pollutants in a monitoring network is nontrivial. Over the last two decades or so, an increasing amount of research has been oriented towards optimal network design, particularly in the area of air quality, for example, Caselton and Zidek (1984); Haas (1992); Pe´rez-Abreu and Rodrı´guez (1996); Zidek et al (2000); Chow et al (2002); Elkamel et al (2008); Pesch et al (2008); Ruiz-Ca´rdenas et al (2010); Zidek and Zimmerman (2010); Saunier et al (2011);Wu and Bocquet (2011);RuizCa´rdenas et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Analysis and evolution of air quality monitoring networks using combined statistical information indexes

Osses

Gallardo

Faundez

2013

Tellus B: Chemical and Physical Meteorology

View full text Add to dashboard Cite

A B S T R A C TIn this work, we present combined statistical indexes for evaluating air quality monitoring networks based on concepts derived from the information theory and KullbackÁLiebler divergence. More precisely, we introduce: (1) the standard measure of complementary mutual information or 'specificity' index; (2) a new measure of information gain or 'representativity' index; (3) the information gaps associated with the evolution of a network and (4) the normalised information distance used in clustering analysis. All these information concepts are illustrated by applying them to 14 yr of data collected by the air quality monitoring network in Santiago de Chile (33.5 S, 70.5 W, 500 m a.s.l.). We find that downtown stations, located in a relatively flat area of the Santiago basin, generally show high 'representativity' and low 'specificity', whereas the contrary is found for a station located in a canyon to the east of the basin, consistently with known emission and circulation patterns of Santiago. We also show interesting applications of information gain to the analysis of the evolution of a network, where the choice of background information is also discussed, and of mutual information distance to the classifications of stations. Our analyses show that information as those presented here should of course be used in a complementary way when addressing the analysis of an air quality network for planning and evaluation purposes.

show abstract

“…Different objectives of optimization for designing monitoring network include minimisation of prediction variance (McKinney and Loucks 1992; Asefa et al 2004Asefa et al , 2005Nunes et al 2004a, b, c;Herrera and Pinder 2005;Ammar et al 2008;Chadalavada and Datta 2008;Dokou and Pinder 2009;Ruiz-Cardenas et al 2010;Chadalavada et al 2011), contaminant detection (Massmann andFreeze 1987a, b;Meyer and Brill 1988;Hudak andLoaiciga 1992, 1993;Datta and Dhiman 1996;Mahar and Datta 1997;Storck et al 1997;Montas et al 2000;Reed et al 2000;Reed and Minsker 2004;Dhar and Datta 2007;Kollat et al 2008; Bashi-Azghadi and Kerachian 2010), minimisation of monitoring cost (Reed et al 2000(Reed et al , 2003Nunes et al 2004a;Reed and Minsker 2004;Wu et al 2006;Kollat and Reed 2007;Kollat et al 2008Kollat et al , 2011, minimisation of mass estimation error (Montas et al 2000;Reed and Minsker 2004;Wu et al 2005Wu et al , 2006Kollat and Reed 2007) etc. Masoumi and Kerachian (2010) applied an entropy theory for the redesign of optimal groundwater quality monitoring networks.…”

Section: Optimal Compliance Monitoring and Feed-back Informationmentioning

confidence: 99%

Review: Simulation-optimization models for the management and monitoring of coastal aquifers

Janardhanan

Datta

2015

Hydrogeol J

View full text Add to dashboard Cite

The literature on the application of simulationoptimization approaches for management and monitoring of coastal aquifers is reviewed. Both sharp-and dispersive-interface modeling approaches have been applied in conjunction with optimization algorithms in the past to develop management solutions for saltwater intrusion. Simulation-optimization models based on sharp-interface approximation are often based on the Ghyben-Herzberg relationship and provide an efficient framework for preliminary designs of saltwater-intrusion management schemes. Models based on dispersiveinterface numerical models have wider applicability but are challenged by the computational burden involved when applied in the simulation-optimization framework. The use of surrogate models to substitute the physically based model during optimization has been found to be successful in many cases. Scalability is still a challenge for the surrogate modeling approach as the computational advantage accrued is traded-off with the training time required for the surrogate models as the problem size increases. Few studies have attempted to solve stochastic coastal-aquifer management problems considering model prediction uncertainty. Approaches that have been reported in the wider groundwater management literature need to be extended and adapted to address the challenges posed by the stochastic coastal-aquifer management problem. Similarly, while abundant literature is available on simulation-optimization methods for the optimal design of groundwater monitoring networks, applications targeting coastal aquifer systems are rare. Methods to optimize compliance monitoring strategies for coastal aquifers need to be developed considering the importance of monitoring feedback information in improving the management strategies.

show abstract

Stochastic search algorithms for optimal design of monitoring networks

Cited by 32 publications

References 25 publications

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

Optimization of Sample Design Sizes and Shapes for Regionalized Variables Using Simulated Annealing

Analysis and evolution of air quality monitoring networks using combined statistical information indexes

Review: Simulation-optimization models for the management and monitoring of coastal aquifers

Contact Info

Product

Resources

About