Optimal estuarine sediment monitoring network design with simulated annealing

Nunes, Luís Saboga; Caeiro, Sandra; Cunha, Maria da Conceição; Ribeiro, Luís

doi:10.1016/j.jenvman.2005.04.024

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…The use of conventional SA has been described by Chimi-Chiadjeu and other researchers [29,30,31]. The SA algorithm process of this paper is as follows: …”

Section: Methodsmentioning

confidence: 99%

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

Wang

Zhou

et al. 2016

IJERPH

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With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.

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“…The use of conventional SA has been described by Chimi-Chiadjeu and other researchers [29,30,31]. The SA algorithm process of this paper is as follows: …”

Section: Methodsmentioning

confidence: 99%

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

Wang

Zhou

et al. 2016

IJERPH

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“…In fact, as it has been remarked by several authors [1,2], the placement of the sampling stations can be considered the most critical factor in the design of any water quality monitoring network. The selection of these optimal sampling points has been addressed by several authors, but mainly from a statistical viewpoint (a geostatistical approach combined with simulated annealing [3,4], fuzzy logic based on a geographic information system [5], multivariate statistical techniques [6], cellular automata-Markov chain models [7], graphical optimization by interpolation via correlation coefficients and standard deviations [8,9], Kriging variance combined with simulated annealing [10], a profile likelihood approach [11], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Design of an Estuarine Water Quality Monitoring Network by Optimal Control Techniques

Álvarez-Vázquez

Martínez

Rodríguez

et al. 2023

Environ Model Assess

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In this work, we propose a novel methodology in order to automatically optimize the location of the sampling points for a water quality monitoring network in an estuary, in such a way that any unknown pollution source can be identified (both in intensity and location) from the data supplied by those sampling points. In the central part of the article, after a rigorous mathematical formulation of the environmental problem, the full details of its numerical implementation are given. Finally, we present and analyze the results when applying the above proposed technique to study a real case in Ría of Vigo (northwestern Spain).

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“…The main target user and the requested wide range of application moved to focus on data-driven algorithms while neglecting physically based methods (e.g., data assimilation and Kalman-Filter based methods). Within this class of methods, the choice fell upon SSA, whose efficacy has been largely proved, both theoretically (Metropolis et al 1953;Kirkpatrick et al 1983;Tsitsiklis 1989;Christakos and Killam 1993;Deutsch and Cockerham 1994;Drosou and Pitoura 2009;Richey 2010) and practically (Pardo-Igùzquiza 1998;Van Groenigen and Stein 1998;Van Groenigen et al 1999, 2000Nunes et al 2006;Nunes et al 2007), by a large amount of scientific literature (Henderson et al 2003).…”

Section: Introductionmentioning

confidence: 99%

MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

Barca

Passarella

Vurro

et al. 2014

Water Resour Manage

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Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

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Optimal estuarine sediment monitoring network design with simulated annealing

Cited by 12 publications

References 39 publications

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

Optimizing the Design of an Estuarine Water Quality Monitoring Network by Optimal Control Techniques

MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

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