Title
Geostatistical modeling of geochemical variables in 3D
Creator
Pejović, Milutin M., 1983-
Copyright date
2016
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
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Language
English
Cobiss-ID
Committee report
Theses Type
Doktorska disertacija
description
Datum odbrane: 09.12.2016.
Other responsibilities
mentor
Bajat, Branislav, 1963-
mentor
Gospavić, Zagorka, 1959-
član komisije
Kilibarda, Milan, 1983-
član komisije
Čakmak, Dragan
član komisije
Hengl, Tomislav
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Beogradu
Faculty
Građevinski fakultet
Title translated
Geostatističko modeliranje geohemijskih promenljivih u 3D prostoru
Publisher
[M. Pejović]
Format
XIV, 138 listova
description
Geodesy - Modeling and Management in Geodesy / Geodezija - Modeliranje i menadzment u geodeziji
Abstract (en)
Geostatistical mapping of soil properties in 3D refers to the application of geostatistical
methods to the soil data in order to produce maps of soil properties at different depths.
Through two separate studies, this thesis elaborates on two different approaches for 3D
soil mapping. At first, the well established Spline-Than-Krige approach for the mapping
of soil pollutants atmospherically deposited from the copper smelting plant, was used. In
the absence of the monitoring data, which can be used for a detailed characterization of the
plume spreading process, this study was confined to the consideration of terrain exposure
to explain spatial trend in arsenic distribution at different depths. This study aims to
explore the extent to which the commonly available information, such as the prevailing
wind direction, or the location of the source of pollution, in combination with the digital
terrain model, can be used to quantify the terrain exposure, and hence to improve the
spatial prediction of the arsenic concentration at several soil depths.
Next, the innovative geostatistical approach to 3D mapping of soil properties, based on
soil profile data, was proposed. It provides the semi-automatic way for 3D modeling of
soil variables, prediction over the regular grids (rasters) and also the evaluation of prediction
accuracy. Methodologically, this approach operates within the 3D regression kriging
framework. 3D trend model is conceptualized as hierarchical or non-hierarchical linear
interaction model. This means that the model includes the interactions between the spatial
covariates and depth in the hiearchial or non-hierarchial manner. The trend modeling
is based on the application of the penalized regression technique, lasso. The lasso uses
a specific regularization penalty in a fitting procedure to enable the efficient parameter
estimation and variable selection (including interaction terms) at the same time...
Abstract (sr)
Geostatistiˇcko kartiranje zemljišta u 3D odnosi se na primenu geostatistiˇckih metoda na
zemljišnim podacima u cilju izrade karata zemljišnih karakteristika jednog podruˇcja, koje
se odnose na razliˇcite dubine zemljišta. U okviru dve nezavisne studije, ova doktorska
disertacija razmatra dva razliˇcita pristupa geostatistiˇckog modeliranja zemljišta u 3D. U
okviru prve studije, "Spline-Than-Krige" metod je koriš´cen za kartiranje koncentracije
arsena u zemljištu, u blizini Rudarsko-topioniˇcarskog basena Bor, na tri razliˇcite dubine
(0-5 cm, 5-15 cm i 15-30 cm). Dugogodišnje emitovanje nepreˇciš´cenih materija iz topionice
rudnika u atmosferu, dovelo je do zagadjenja zemljišta u okolini, taloženjem štetnih
materija nošenih vetrom. U odsustvu podataka kojima bi se detaljnije mogao opisati proces
raspršivanja štetnih materija, ova studija se ograniˇcila na analizu izloženosti terena
uticaju vetra, a time i procesu zagad¯enja. Predstavljen je inovativan pristup kvantifikaciji
izloženosti terena izvoru zagad¯enja. Na osnovu opšte dostupnih podataka, kreirano je
nekoliko parametara kojima se kvantifikuje geometrijska i topografska izloženost svake
tacˇke terena izvoru zagad¯enja. Tako kreirani parametri, iskorišc´eni su za opisivanje prostornog
trenda koncentracije arsena na tri razliˇcite dubine. Definisani trendovi, koriš´ceni su
u okviru regresionog kriginga, za prostornu predikciju. Na taj naˇcin pokušalo se odgovoriti
na pitanje, u kojoj meri, opšte dostupni podaci, kao što su pravac dominantnog vetra
ili poznavanje taˇcne lokacije izvora zagadjenja u kombinaciji sa digitalnim modelom terena,
mogu biti iskoriš´ceni da bi se unapredila preciznost prostorne predikcije zemljišnih
zagadjivaˇca, kako na površinskim slojevima tako i na ve´cim dubinama...
Authors Key words
3D soil maping, 3D regression kriging, Spline-Than-Krige, lasso, nested
cross-validation, pollution assessment, topographic exposure
Authors Key words
3D modeliranje zemljišta, 3D regresioni kriging, lasso, ugnježdena unakrsna
validacija, procena zagad¯enosti, topografska izloženost
Type
Tekst
Abstract (en)
Geostatistical mapping of soil properties in 3D refers to the application of geostatistical
methods to the soil data in order to produce maps of soil properties at different depths.
Through two separate studies, this thesis elaborates on two different approaches for 3D
soil mapping. At first, the well established Spline-Than-Krige approach for the mapping
of soil pollutants atmospherically deposited from the copper smelting plant, was used. In
the absence of the monitoring data, which can be used for a detailed characterization of the
plume spreading process, this study was confined to the consideration of terrain exposure
to explain spatial trend in arsenic distribution at different depths. This study aims to
explore the extent to which the commonly available information, such as the prevailing
wind direction, or the location of the source of pollution, in combination with the digital
terrain model, can be used to quantify the terrain exposure, and hence to improve the
spatial prediction of the arsenic concentration at several soil depths.
Next, the innovative geostatistical approach to 3D mapping of soil properties, based on
soil profile data, was proposed. It provides the semi-automatic way for 3D modeling of
soil variables, prediction over the regular grids (rasters) and also the evaluation of prediction
accuracy. Methodologically, this approach operates within the 3D regression kriging
framework. 3D trend model is conceptualized as hierarchical or non-hierarchical linear
interaction model. This means that the model includes the interactions between the spatial
covariates and depth in the hiearchial or non-hierarchial manner. The trend modeling
is based on the application of the penalized regression technique, lasso. The lasso uses
a specific regularization penalty in a fitting procedure to enable the efficient parameter
estimation and variable selection (including interaction terms) at the same time...
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