Generalized coupled Markov chain model for characterizing categorical variables in soil mapping

Eungyu Park, Amro M.M. Elfeki, Yun Goo Song, Kangjoo Kim

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60%). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.

Original languageEnglish
Pages (from-to)909-917
Number of pages9
JournalSoil Science Society of America Journal
Volume71
Issue number3
DOIs
Publication statusPublished - 2007 May 1

Fingerprint

soil surveys
Markov chain
soil
soil types
soil type
simulation models
conditioning
simulation
soil sampling
spatial distribution
lithology

All Science Journal Classification (ASJC) codes

  • Soil Science

Cite this

@article{ffe31c9dae024ceb8497bb8644b837ec,
title = "Generalized coupled Markov chain model for characterizing categorical variables in soil mapping",
abstract = "We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60{\%}). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.",
author = "Eungyu Park and Elfeki, {Amro M.M.} and Song, {Yun Goo} and Kangjoo Kim",
year = "2007",
month = "5",
day = "1",
doi = "10.2136/sssaj2005.0386",
language = "English",
volume = "71",
pages = "909--917",
journal = "Soil Science Society of America Journal",
issn = "0361-5995",
publisher = "Soil Science Society of America",
number = "3",

}

Generalized coupled Markov chain model for characterizing categorical variables in soil mapping. / Park, Eungyu; Elfeki, Amro M.M.; Song, Yun Goo; Kim, Kangjoo.

In: Soil Science Society of America Journal, Vol. 71, No. 3, 01.05.2007, p. 909-917.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Generalized coupled Markov chain model for characterizing categorical variables in soil mapping

AU - Park, Eungyu

AU - Elfeki, Amro M.M.

AU - Song, Yun Goo

AU - Kim, Kangjoo

PY - 2007/5/1

Y1 - 2007/5/1

N2 - We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60%). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.

AB - We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60%). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.

UR - http://www.scopus.com/inward/record.url?scp=34249098746&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34249098746&partnerID=8YFLogxK

U2 - 10.2136/sssaj2005.0386

DO - 10.2136/sssaj2005.0386

M3 - Article

VL - 71

SP - 909

EP - 917

JO - Soil Science Society of America Journal

JF - Soil Science Society of America Journal

SN - 0361-5995

IS - 3

ER -