Proposta de um teste de hipótese para a existência de dependência espacial em dados geoestatísticos

Seidel, Enio Júnior; Oliveira, Marcelo Silva de

doi:10.1590/s1982-21702014000400043

Cited by 4 publications

(4 citation statements)

References 7 publications

(3 reference statements)

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“…This behavior is different from the factors of model to the spherical, exponential and Gaussian models, which are fixed in each model, assuming, respectively, the values 0.375, 0.317 and 0.504 (Seidel, Oliveira, 2014a;2014b;. The maximum FM * that can be obtained in the power model is close to 0.667 (when β is close to 2).…”

Section: Resultsmentioning

confidence: 85%

“…The SPD * index is calculated based on an adaptation of the concept of spatial dependence areas (Seidel, Oliveira, 2014b;. The index is obtained as the ratio between the equivalent observed spatial dependence area (SDA where SDA * observed is given as the integral of the difference between equivalent sill and the power model; the SDA * maximum is given as the integral of the difference between equivalent sill and the adapted power model (C0 = 0).…”

Section: The Construction Of the Spd * Indexmentioning

confidence: 99%

“…The factor of model (FM), according to Seidel, Oliveira (2014a;2014b), can be understood as a value that expresses the strength of spatial dependence that the model can achieve.…”

Section: Introductionmentioning

confidence: 99%

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Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

et al. 2017

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Abstract:In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * , for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.Keywords: Geostatistics; Variographic analysis; Semivariogram without sill; Spatial dependence indexes. Resumo:Em geoestatística, a utilização de medidas que descrevam a dependência espacial do atributo é de grande importância, porém apenas alguns modelos (que possuem estacionariedade de segunda ordem) são contemplados com tais medidas. Assim, este trabalho tem como objetivo propor medidas para avaliação do grau de dependência espacial em fenômenos com ajuste de modelo Barbosa, I. C. et al. 462 Bull. Geod. Sci, Articles section, Curitiba, v. 23, n°3, p.461 -475, Jul -Sept, 2017. potência. A partir de uma premissa que considera o patamar-equivalente como o valor de semivariância que coincide com o ponto em que as curvas ajustadas do modelo potência se interceptam, pode-se construir dois índices para avaliação de tal dependência. O primeiro, DE * , é obtido a partir da relação entre a contribuição-equivalente (α) e o patamar-equivalente (C * = C0 + α), e varia de 0 a 100% (baseado no cálculo de áreas de dependência espacial). O segundo, IDE * , além da relação anterior, considera um fator de modelo equivalente (FM * ), que depende do expoente β, o qual descreve a força da dependência espacial no modelo potência (baseado em áreas de correlação espacial). O IDE * , para β próximo de 2, assume sua maior escala, variando de 0 a 66.67%. Ambos os índices possuem distribuição simétrica, e permitem a classificação da dependência espacial em fraca, moderada e forte.

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Section: Resultsmentioning

confidence: 85%

Section: The Construction Of the Spd * Indexmentioning

confidence: 99%

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Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

et al. 2017

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“…In Equations 5, 6 and 7 we note each model has a specific constant in its respective SCA expression. This constant, inherent to each model, is the model factor (MF) that reflects the strength of the specific spatial dependence (Seidel, Oliveira, 2014a;2014b;.…”

Section: Methodsmentioning

confidence: 99%

Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

et al. 2018

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This study aims to propose a spatial dependence index (and its classification), from the concept of spatial correlation areas, for the Cubic, Pentaspherical and Wave models. The index, called Spatial Dependence Index (SDI), covers the following parameters: the range (a), the nugget effect (C 0 ) and the contribution (C 1 ), beyond considering the maximum distance (MD) between sampled points and the model factor (MF). The proposed index, unlike the most used in the literature, considers the influence of the range parameter to describe the spatial dependence, highlighting the importance of this formulation. The spatial dependence classification, based on the observed asymmetric behavior in the SDI, was performed considering categorizations from the median and the 3rd quartile of the index. We obtain the spatial dependence classification in terms of weak, moderate, and strong, just as it is usually described in literature.

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Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

2018

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Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.

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Proposta de um teste de hipótese para a existência de dependência espacial em dados geoestatísticos

Cited by 4 publications

References 7 publications

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

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