Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

Juang, Kai‐Wei; Chen, Yue-Shin; Lee, Dar‐Yuan

doi:10.1016/j.envpol.2003.07.001

Cited by 141 publications

(104 citation statements)

References 13 publications

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“…As a common factor for all these methods, the interpolated value is estimated by a linear weighted combination of near values where weights and number of used values are dependent on the correlation between existing values [61]. Combinations of kriging methods with Sequential Gaussian Simulation (SGS, [62]) or Sequential Indicator Simulation (SIS, [63]) allows estimation of uncertainty, and the distribution of the predictions.…”

Section: Methods Based On Krigingmentioning

confidence: 99%

Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

Lepot

Aubin

Clemens

2017

Water

189

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Abstract:A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed.

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Section: Methods Based On Krigingmentioning

confidence: 99%

Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

Lepot

Aubin

Clemens

2017

Water

189

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“…5). Vklju~uje vse podatke v dosegu, dolo~ene na semivariogramu, originalne podatke in že prej simulirane vrednosti (Juang et al, 2004) in uporablja algoritem, ki temelji na vsaki celici posebej (deutscH, 1999). Je metoda, ki spada med naklju~ne (stohasti~ne) simulacije.…”

Section: Sekven~na Indikatorska Simulacijaunclassified

Perched aquifers spatial model: a case study for Ljubljansko polje (central Slovenia)

Šram¹

2012

Geologija

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Izvle~ekVodonosnik Ljubljanskega polja je eden izmed najve~jih in najpomembnej{ih vodonosnikov v Sloveniji. V ve~jem delu je vodonosnik odprt, na nekaterih obmo~jih, v jugozahodnem delu pa se pojavljajo slab{e prepustne plasti, kjer nastanejo vise~i vodonosniki. Na podlagi obdelave litolo{kih podatkov iz vrtin je bil s pomo~jo geostatisti~nega modeliranja s sekven~no indikatorsko simulacijo izdelan hidrogeolo{ki model zahodnega dela Ljubljanskega polja s poudarkom na prepustnosti litolo{kih enot. Iz hidrogeolo{kega modela so bile izlo~ene slab{e prepustne plasti s koeficientom prepustnosti K < 10 -6 m/s in povr{ino ve~jo od 0,07 km 2 , ki so potencialna obmo~ja za pojav vise~ih vodonosnikov. Rezultati modela kažejo najve~jo koncentracijo slab{e prepustnih plasti pod vznožjem Ši{enskega in Grajskega hriba. AbstractLjubljansko polje aquifer is one of the biggest and most important aquifers in Slovenia. In general it is an unconfined aquifer, but locally, on the SW part, layers with low hydraulic conductivity appear and on those areas perched aquifers are formed. Based on lithological data from boreholes (emphasizing hydraulic conductivity of lithological units) and use of geostatistical method Sequential Indicator Simulation (SIS) hydrogeological model of western part of Ljubljansko polje was made. In the model layers with hydraulic conductivity K < 10 -6 m/s and area larger than 0.07 km 2 were defined as potential areas of perched aquifers. The majority of them are located on the foothills of Ši{enski and Grajski hrib. UvodVodonosnik Ljubljanskega polja je eden izmed najve~jih vodonosnikov v Sloveniji. Iz njega se s pitno vodo oskrbuje preko 300.000 prebivalcev Ljubljane in okolice, zato je njegova za{~ita in poznavanje njegovih geolo{kih lastnosti izjemnega pomena.Vodonosnik je tema raziskav že vse od konca 19. stoletja in je zaradi tega že zelo dobro raziskan, njegove karakteristike in možnosti izkori{~anja so dobro opredeljene, kljub temu pa je zaradi pestre geologije {e veliko neznanega oziroma neraziskanega. Med slabo raziskane pojave spadajo tudi vise~i vodonosniki.Vodonosnik je razvit v kotlini, ki je zasuta s kvartarnim re~nim prodom in peskom z vmesnimi le~ami melja, gline in konglomerata (Žlebnik, 1971). Slab{e prepustne plasti med bolj prepustnimi plastmi omogo~ajo nastanek vise~ih vodonosnikov. Iz dosedanjega poznavanja hidrogeolo{kih lastnosti vodonosnika Ljubljanskega polja se domneva, da je porazdelitev vise~ih vodonosnikov pomembna s stali{~a vpliva na kemijsko stanje podzemne vode in na raz{irjanje onesnaževal, vendar pa je njihovo trenutno poznavanje relativno omejeno.Namen izdelave prostorskega modela vise~ih vodonosnikov je dolo~itev razprostiranja slab{e prepustnih plasti znotraj dobro prepustnega nezasi~enega dela vodonosnika v zahodnem delu Ljubljanskega polja. Na podlagi obstoje~ih podat kov o že znanih vise~ih vodonosnikih in litolo{kih popisov vrtin so bila dolo~ena potencialna obmo~ja vise~ih vodonosnikov. Obravnavano obmo~jeObravnavano obmo~je se nahaja v osrednji Sloveniji in je del ...

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“…O procedimento consiste na contabilização do número de realizações em que todos os locais aparecem simultaneamente simulados acima do valor crítico. Para mais detalhes, veja Juang et al (2004).…”

Section: Methodsunclassified

“…A simulação sequencial gaussiana (SSG) e a simulação sequencial indicatriz (SSI) são os tipos mais comuns de simulação estocástica. A SSG é a mais utilizada em estudos sobre propriedades químicas do solo (Bourennane et al, 2007;Chai et al, 2007;Delbari et al, 2009;Zhao et al, 2009;Sun et al, 2012), enquanto a SSI (Delbari et al, 2010) é frequentemente utilizada na área ambiental (Juang et al, 2004;Zhao et al, 2005). Ao contrário da KO, os modelos de simulação estocástica reproduzem a variabilidade espacial dos dados, o que elimina o efeito de suavização.…”

Section: Introductionunclassified

Modelagem e quantificação da incerteza espacial do potássio disponível no solo por simulações estocásticas

Oliveira

Teixeira

Panosso

et al. 2014

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o desempenho da simulação sequencial gaussiana (SSG) e da simulação sequencial indicatriz (SSI) na modelagem da incerteza das predições do K disponível em área de cana-de-açúcar, e comparar as simulações com o método já consagrado de krigagem ordinária (KO). Uma malha amostral com 626 pontos foi instalada em área de 200 ha, no Município de Tabapuã, em São Paulo. As simulações reproduziram a variabilidade dos dados amostrais de K disponível, enquanto a KO superestimou os baixos teores de K e subestimou os altos. O mapa de desvio-padrão obtido a partir da KO mostrou menor variação ao longo da área de estudo, quando comparado aos mapas obtidos a partir das simulações. A SSI obteve acurácia 22% superior à obtida pela SSG, na modelagem da função de distribuição condicional do K. As simulações apresentam maior eficiência que a KO para modelar incerteza na distribuição espacial do K. A SSI apresenta melhor desempenho que a SSG na estimativa dos teores de K disponível, em área de cana-de-açúcar.Termos para indexação: fertilidade do solo, geoestatística, krigagem, simulação sequencial gaussiana, simulação sequencial indicatriz, variabilidade espacial. Modeling and quantification of available potassium spatial uncertainty in the soil by stochastic simulationsAbstract -The objective of this work was to evaluate the performance of the sequential Gaussian simulation (SGS) and the sequential indicator simulation (SIS) for modeling the uncertainty of available K predictions in a sugarcane area, and to compare both simulations to the already established method of ordinary kriging (OK). A sampling grid with 626 points was installed in an area of 200 ha, in the municipality of Tabapuã, in the state of São Paulo, Brazil. The simulations reproduced the variability in the available K sample data, whereas OK overestimated the low K levels and underestimated the high ones. The standard deviation map obtained from OK showed less variation along the studied area when compared to the maps obtained from the simulations. SIS achieved an accuracy 22% higher than that obtained by SGS for modeling the conditional distribution function of K. The simulations have higher efficiency than OK for modeling the uncertainty in the spatial distribution of K. SIS has better performance than SGS for estimating the levels of available K in sugarcane area.Index terms: soil fertility, geostatistics, kriging, sequential Gaussian simulation, sequential indicator simulation, spatial variability. IntroduçãoO Estado de São Paulo é o maior produtor de canade-açúcar (Saccharum officinarum L.), com 51,7% da área total cultivada no Brasil, segundo dados da Companhia Nacional do Abastecimento (2014). A cultura consome aproximadamente 13% do total de fertilizantes utilizado anualmente no país (Otto et al., 2010), e o potássio é o nutriente mais absorvido.Os solos tropicais brasileiros apresentam baixos teores de potássio na forma disponível (Benites et al., 2010), e a cana-de-açúcar responde bem à aplicação do nutriente. O...

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Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

Cited by 141 publications

References 13 publications

Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

Perched aquifers spatial model: a case study for Ljubljansko polje (central Slovenia)

Modelagem e quantificação da incerteza espacial do potássio disponível no solo por simulações estocásticas

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