Optimization of sample design sizes and shapes for regionalized variables using simulated annealing

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 are...

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Autores principales:	Guedes,Luciana P.C, Uribe-Opazo,Miguel A, Ribeiro Junior,Paulo J
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2014
Materias:	Geostatistics interpolation precision agriculture spatial variability
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100004
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id	oai:scielo:S0718-16202014000100004
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spelling	oai:scielo:S0718-162020140001000042014-10-10Optimization of sample design sizes and shapes for regionalized variables using simulated annealingGuedes,Luciana P.CUribe-Opazo,Miguel ARibeiro Junior,Paulo J Geostatistics interpolation precision agriculture spatial variability 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.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.41 n.1 20142014-04-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100004en10.4067/S0718-16202014000100004
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Geostatistics interpolation precision agriculture spatial variability
spellingShingle	Geostatistics interpolation precision agriculture spatial variability Guedes,Luciana P.C Uribe-Opazo,Miguel A Ribeiro Junior,Paulo J Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
description	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.
author	Guedes,Luciana P.C Uribe-Opazo,Miguel A Ribeiro Junior,Paulo J
author_facet	Guedes,Luciana P.C Uribe-Opazo,Miguel A Ribeiro Junior,Paulo J
author_sort	Guedes,Luciana P.C
title	Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
title_short	Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
title_full	Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
title_fullStr	Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
title_full_unstemmed	Optimization of sample design sizes and shapes for regionalized variables using simulated annealing
title_sort	optimization of sample design sizes and shapes for regionalized variables using simulated annealing
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2014
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100004
work_keys_str_mv	AT guedeslucianapc optimizationofsampledesignsizesandshapesforregionalizedvariablesusingsimulatedannealing AT uribeopazomiguela optimizationofsampledesignsizesandshapesforregionalizedvariablesusingsimulatedannealing AT ribeirojuniorpauloj optimizationofsampledesignsizesandshapesforregionalizedvariablesusingsimulatedannealing
_version_	1714202154389995520

Optimization of sample design sizes and shapes for regionalized variables using simulated annealing

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