Interpolation techniques and associated software for environmental data

Akkala, Arjun; Devabhaktuni, Vijay; Kumar, Ashok

doi:10.1002/ep.10455

Cited by 57 publications

(42 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The same happened to the ANNs developed for spatial interpolation of KE>25 for May (KE computed by WM equation). Akkala et al (2010), ANN interpolators work well with sparse data irregularly distributed, just as for the data presented (Figure 1). The ANNs, in order to have better performance, need consistent training and the data-set used must represent the nuances of the terrain to be modeled (Teegavarapu 2007, Miranda et al 2009, Sivapragasam et al 2010, as was the case in this study.…”

Section: Dif = Percentage Difference Of the Values Computed For Eachsupporting

confidence: 54%

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Cecílio

Moreira

Pezzopane

et al. 2013

An. Acad. Bras. Ciênc.

View full text Add to dashboard Cite

The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI 30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI 30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI 30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.

show abstract

Section: Dif = Percentage Difference Of the Values Computed For Eachsupporting

confidence: 54%

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Cecílio

Moreira

Pezzopane

et al. 2013

An. Acad. Bras. Ciênc.

View full text Add to dashboard Cite

show abstract

“…In the corresponding biological climate condition, the surface of the original rock is gradually turned into soil through physical and chemical weathering, and biological degradation [31][32][33][34]. We found that parent materials and pedogenic processes were major factors contributing to high concentrations of heavy metals in soil [26].…”

Section: Resultsmentioning

confidence: 99%

Spatial Distribution and Influence Analysis of Soil Heavy Metals in a Hilly Region of Sichuan Basin

Liu¹,

Liu²,

Yun-fan³

et al. 2017

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

“…A superioridade da krigagem sobre o IPD explica-se pelo fato de que a primeira é indicada para a aplicação em regiões onde a distribuição espacial dos dados observados é boa e sem descontinuidades significativas (Tabela 3). O IPD é indicado para locais com distribuição espacial boa e com alta densidade de estações (Akkala et al, 2010), o que não é o caso do presente trabalho. Além disso, a krigagem, a partir dos dados de entrada, determina os melhores parâmetros para modelagem da função matemática de interpolação, por meio do ajuste do semivariograma, o que resulta em boas estimativas, quando os dados são bem representativos da região a ser modelada (Sivapragasam et al, 2010).…”

Section: Resultsunclassified

Método para a espacialização dos elementos do balanço hídrico climatológico

Cecílio

Silva

Xavier

et al. 2012

Pesq. agropec. bras.

View full text Add to dashboard Cite

Resumo -O objetivo deste trabalho foi desenvolver e avaliar um método de espacialização dos elementos do balanço hídrico climatológico (BHC). O método utiliza modelos digitais do terreno que contêm a distribuição espacial dos elementos climáticos de entrada do BHC. A avaliação foi feita com dados reais de BHC referentes a 110 estações pluviométricas do Estado do Espírito Santo. Comparou-se o desempenho do método proposto com o dos seguintes interpoladores espaciais: krigagem e inverso de uma potência da distância. O método proposto apresentou melhores estimativas espaciais de todos os elementos do balanço hídrico -evapotranspiração real, excedente, deficiência e disponibilidade hídrica.Termos para indexação: deficiência hídrica, excedente hídrico, interpolação, krigagem. Method for spatialization of the climatic water balance elementsAbstract -The objective of this work was to develop and evaluate a method for the spatial distribution of the climatic water balance (CWB) elements. The method applies terrain digital models containing the spatial distribution of climate elements used as input data of CWB. Evaluation was made with existing CWB data from 110 pluviometric stations of Espírito Santo state, Brazil. The proposed method's performance was compared to the one of the following spatial interpolators: kriging and inverse distance weighting. The proposed method presented best predictions of the spatial distribution of all CWB elements -actual evapotranspiration, water deficit, water surplus and water availability.

show abstract

Interpolation techniques and associated software for environmental data

Cited by 57 publications

References 29 publications

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Spatial Distribution and Influence Analysis of Soil Heavy Metals in a Hilly Region of Sichuan Basin

Método para a espacialização dos elementos do balanço hídrico climatológico

Contact Info

Product

Resources

About