On the application of Bayesian Networks in Digital Soil Mapping

Geoderma

Volumn 259-260, Issue , 2015, Pages 134-148

  Taalab, K ^a   Corstanje, R ^b   Zawadzka, J ^b   Mayr, T ^b   Whelan, M J ^c   Hannam, J A ^b   Creamer, R ^d  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b CRANFIELD UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF LEICESTER   (United Kingdom)

^d TEAGASC   (Ireland)

Author keywords

Bayesian Networks; Bulk density; Expert knowledge; Mapping; Modelling; Soil

Indexed keywords

FORECASTING; MAPPING; MODELS; PROBABILITY; SOILS;

APPLICATION OF BAYESIAN NETWORKS; BULK DENSITY; CAUSE-AND-EFFECT RELATIONSHIPS; DIGITAL SOIL MAPPINGS; EXPERT KNOWLEDGE; NETWORK FRAMEWORKS; PROBABILISTIC MODELS; SOIL-LANDSCAPE RELATIONSHIPS;

BAYESIAN NETWORKS;

BAYESIAN ANALYSIS; GRAPHICAL METHOD; NUMERICAL MODEL; PROBABILITY; SOIL ANALYSIS;

EID: 84930943976     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2015.05.014     Document Type: Article

References (92)

1. Aalders I., Hough R.L., Towers W. Risk of erosion in peat soils - an investigation using Bayesian belief networks. Soil Use Manag. 2011, 27:538-549.
2. Aguilera P.A., Fernandez A., Fernandez R., Rumi R., Salmeron A. Bayesian Networks in environmental modelling. Environ. Model. Softw. 2011, 26(12).
3. Agyare W.A., Park S.J., Vlek P.L.G. Artificial neural network estimation of saturated hydraulic conductivity. Vadose Zone J. 2007, 6:423-431.
4. Aitkenhead M.J., Aalders I.H. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. J. Environ. Manag. 2009, 90:236-250.
5. Avery B.W. Soil classification for England and Wales [higher categories]. Technical Monograph, Soil Survey of England and Wales 1980, 14.
6. Bayes T. Essay towards solving a problem in the doctrine of chances. Philos. Trans. R. Soc. 1783, 53:370-418.
7. Borsuk M.E. Ecological Informatics: Bayesian Networks. Encyc Ecol 2008, 307-317. Elsevier. S.E. Jorgensen, B. Fath (Eds.).
8. Braakhekke M.C., Wutzler T., Beer C., Kattge J., Schrumpf M., Schöning I., Hoosbeek M.R., Kruijt B., Kabat P.And, Reichstein M. Modeling the vertical soil organic matter profile using Bayesian parameter estimation. Biogeosci. Discuss. 2012, 9:11239-11292.
9. Bui E.N., Lougldead A., Corner R. Extracting soil-landscape rules from previous soil surveys. Aust. J. Soil Res. 1999, 37(3):495-508.
10. Cain J. Planning improvements in natural resources management. Guidelines for Using Bayesian Networks to Support the Planning and Management of Development Programmes in the Water Sector and Beyond 2001, Centre for Ecology and Hydrology, Wallingford, UK.
11. Calhoun F.G., Smeck N.E., Slater B.L., Bigham J.M., Hall G.F. Predicting bulk density of Ohio soils from morphology, genetic principles, and laboratory characterization data. Soil Sci. Soc. Am. J. 2001, 65:811-819.
12. Carré F., McBratney A.B., Mayr T., Montanarella L. Digital soil assessments: beyond DSM. Geoderma 2007, 142(1-2):69-79.
13. Charniak E. Bayesian Networks without tears. AI Mag. 1991, 12(4):50.
14. Chen S.H., Pollino C.A. Good practice in Bayesian Network modelling. Environ. Model. Softw. 2012, 37:134-145.
15. Cook S.E., Corner R.J., Grealish G., Gessler P.E., Chartres C.J. A rule-based system to map soil properties. Soil Sci. Soc. Am. J. 1996, 60:1893-1900.
16. CORINE Corine Land Cover 2000 seamless vector data 2000, European Environment Agency, (http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database-4).
17. Corner R.J., Hickey R.J., Cook S.E. Knowledge based soil attribute mapping in GIS: the Expector method. Trans. GIS 2002, 6(4):383-402.
18. Correa M., Bielza C., Pamies-Teixeira J. Comparison of Bayesian Networks and artificial neural networks for quality detection in a machining process. Expert Syst. Appl. 2009, 36(3):7270-7279.
19. Corstanje R., Grunwald S., Lark R.M. Inferences from fluctuations in the local variogram about the assumption of stationarity in the variance. Geoderma 2008, 143:123-132.
20. Daly K., Fealy R. Digital Soil Information System for Ireland Scoping Study (2005-S-DS-22-M1) Final Report 2007, Environmental Protection Agency, Ireland.
21. Degroot M.H. A Bayesian view of assessing uncertainty and comparing expert opinion. J. Stat. Plan. Infer. 1988, 20(3).
22. Dikau R., Brabb E.E., Mark R.M. Landform classification of New Mexico by computer. U.S. Geological Survey Open File Report 91-634 1991.
23. Dlamini W.M. A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland. Environ. Model. Softw. 2010, 25(2).
24. Dobos E., Daroussin J., Montanarella L. An SRTM-based procedure to delineate SOTER Terrain Units on 1:1 and 1:5 million scales, EUR 21571 EN 2005, Office for Official Publications of the European Communities, Luxembourg, (55 pp., http://eusoils.jrc.it/ESDB_Archive/eusoils_docs/Images/EUR21571EN.pdf).
25. Dougherty J., Kohavi R., Sahami M. Supervised and unsupervised discretization of continuous features. Proc 12th Int Con Machine Learn 1995, 194-202. Morgan Kaufmann, San Francisco, CA. A. Prieditis, S. Russell (Eds.).
26. Duda R.O., Hart P.E. Pattern Classification and Scene Analysis 1973, John Wiley & Sons, New York.
27. ESRI ArcGIS 10.1 Geographical Information System 2011, ESRI, Redlands, California.
28. Fayyad U.M., Irani K.B. Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence 1993, 1022-1027. Morgan Kaufmann, San Francisco, CA.
29. Fealy R.M., Green S., Loftus M., Meehan R., Radford T., Cronin C., Bulfin M. Teagasc EPA Soils and Subsoils Mapping Project - Final Report. Volume I, Teagasc, Dublin 2009.
30. Finke P.A. On digital soil assessment with models and the Pedometrics agenda. Geoderma 2012, 171:3-15.
31. Friedman N., Goldzmidt M. Discretizing continuous attributes while learning Bayesian Networks. Proceedings of the 13th International Conference on Machine Learning (ICML) 1996, 157-165. Morgan Kaufmann Publishers, San Francisco, CA.
32. Friedman N., Geiger D., Goldszmidt M. Bayesian Network classifiers. Mach. Learn. 1997, 29.
33. Fuller R.M., Smith G.M., Sanderson J.M., Hill R.A., Thomson A.G. The UK Land Cover Map 2000: construction of a parcel-based vector map from satellite images. Cartogr. J. 2002, 39:15-25.
34. Gardiner M.J., Radford T. Ireland: general soil map. An Foras Talúntais (now Teagasc), Dublin, Ireland 1980, 2nd ed.
35. Gardiner M.J., Ryan P. A new generalised soil map of Ireland and its land-use interpretation. Ir. J. Agric. Res. 1969, 95-109.
36. Geological Survey of Ireland http://www.gsi.ie/.
37. Grimm R., Behrens T., Maerker M., Elsenbeer H. Soil organic carbon concentrations and stocks on Barro Colorado Island - digital soil mapping using Random Forests analysis. Geoderma 2008, 146:102-113.
38. Grinand C., Arrouays D., Laroche B., Martin M.P. Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma 2008, 143:180-190.
39. Grunwald S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 2009, 152:195-207.
40. Hallett S.H., Jones R.J.A., Keay C.A. Environmental information systems developments for planning sustainable land use. Int. J. Geogr. Inf. Sci. 1996, 10:47-64.
41. Hallett S.H., Hollis J.M., Keay C.A. Derivation and evaluation of a set of pedogenically-based empirical algorithms for predicting bulk density in British soils http://www.landis.org.uk/downloads/index.cfm_Predicting_Bulk_Density.pdf.
42. Hartemink A.E., McBratney A. A soil science renaissance. Geoderma 2008, 148:123-129.
43. Hollis J.M., Hannam J., Bellamy P.H. Empirically-derived pedotransfer functions for predicting bulk density in European soils. Eur. J. Soil Sci. 2012, 63:96-109.
44. Hough R.L., Towers W., Aalders I. The risk of peat erosion from climate change: land management combinations: an assessment with Bayesian belief networks. Hum. Ecol. Risk. Assess. 2010, 16:962-976.
45. IUSS Working Group WRB World Reference Base for Soil Resources. World Soil Resources Rep 2006, 103. FAO, Rome. second ed.
46. Iwahashi J., Pike R.J. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology 2007, 86:409-440.
47. Jakeman A.J., Letcher R.A., Norton J.P. Ten iterative steps in development and evaluation of environmental models. Environ. Model. Softw. 2006, 21:602-614.
48. Jalabert S.S.M., Martin M.P., Renaud J., Boulonne L., Jolivet C., Montanarella L., Arrouays D. Estimating forest soil bulk density using boosted regression modelling. Soil Use Manag. 2010, 26:516-528.
49. Jenny H. Factors of Soil Formation 1941, McGraw-Hill, New York, USA.
50. Jensen F.V. An Introduction to Bayesian Networks 1996, UCL Press, London.
51. Jensen F.V. Bayesian Networks and Decision Graphs 2001, Springer-Verlag, New York.
52. Jiang L., Zhang H., Cai Z., Su J. Learning tree augmented naive Bayes for ranking. Proceedings of the 10th International Conference on Database Systems for Advanced Applications 2005, 688-698. Springer-Verlag, Berlin.
53. Johnson S., Low-Choy S., Mengersen K. Integrating Bayesian Networks and geographic information systems: good practice examples. Integr. Environ. Assess. Manag. 2012, 8.
54. Kelly R.A., Jakeman A.J., Barreteau O., Borsuk M.E., ElSawah S., Hamilton S.H., Henriksen H.J., Kuikka S., Maier H.R., van Rizzoli A.E., Delden H., Voinov A. Selection among five common modelling approaches for integrated environmental assessment and management. Environ. Model. Softw. 2013, 47:159-181.
55. Krueger T., Page T., Hubacek K., Smith L., Hiscock K. The role of expert opinion in environmental modelling. Environ. Model. Softw. 2012, 36:2012.
56. Kuhnert P.M., Hayes K.R. How believable is your BBN?. 18th World IMACs/MODISM Congress, Cairns, Australia, 13-17 July 2009 2009.
57. Lemercier B., Lacoste M., Loum M., Walter C. Extrapolation at regional scale of local soil knowledge using boosted classification trees: a two-step approach. Geoderma 2012, 171:1-98.
58. Liaw A., Wiener M. Classification and Regression by randomForest. R News 2002, ((http://cran.r-project.org/doc/Rnews/), 2, 18-22).
59. MacMillan R.A. LandMapR© Software Toolkit-C++ Version: Users Manual 2003, LandMapper Environ. Sol. Inc., Edmonton, AB, (110 pp.).
60. Marcot B.G., Steventon J.D., Sutherland G.D., McCann R.K. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Can. J. For. Res. 2006, 36:3063-3074.
61. Martin M.P., Lo Seen D., Boulonne L., Jolivet C., Nair K.M., Bourgeon G., Arrouays D. Optimizing pedotransfer functions for estimating soil bulk density using boosted regression trees. Soil Sci. Soc. Am. J. 2009, 73:485-493.
62. Mayr T., Rivas-Casado M., Bellamy P., Palmer R., Zawadzka J., Corstanje R. Two methods for using legacy data in digital soil mapping. Digital Soil Mapping: Bridging Research, Environmental Application, and Operation 2010, 191-202. Springer, Dordrecht. J.L. Boettinger, D.W. Howell, A.C. Moore, A.E. Hartemink, S. Kienast-Brown (Eds.).
63. McBratney A.B., Odeh I.O.A. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 1997, 77.
64. McBratney A.B., Santos M.L.M., Minasny B. On digital soil mapping. Geoderma 2003, 117:3-52.
65. McCann R.K., Marcot B.G., Ellis R. Bayesian belief networks: applications in ecology and natural resource management. Can. J. For. Res. 2006, 36:3053-3062.
66. McCloskey J.T., Lilieholm R.J., Cronan C. Using Bayesian belief networks to identify potential compatibilities and conflicts between development and landscape conservation. Landsc. Urban Plan. 2011, 101:190-203.
67. McGrath S.P., Loveland P.J. The Geochemical Atlas of England and Wales 1992, 101. Blackie, London.
68. Montanarella L., Jones R.J.A., Dusart J. The European Soil Bureau Network. European Soil Bureau Research Report No. 9, EUR 20559 EN 2005, 3-14. second edition. R.J.A. Jones, B. Houskova, P. Bullock, L. Montanarella (Eds.).
69. Moran C.J., Bui E.N. Spatial data mining for enhanced soil map modelling. Int. J. Geogr. Inf. Sci. 2002, 16:533-549.
70. Moreira C.S., Brunet D., Verneyre L., Sa S.M.O., Galdos M.V., Cerri C.C., Bernoux M. Near infrared spectroscopy for soil bulk density assessment. Eur. J. Soil Sci. 2009, 60:785-791.
71. Myllymäki P., Silander T., Tirri H., Uronen P. B-Course: a web-based tool for Bayesian and causal data analysis. Int. J. Artif. Intell. Tools 2002, 11(3):369-387.
72. Nadkarni S., Shenoy P.P. A causal mapping approach to constructing Bayesian Networks. Decis. Support. Syst. 2004, 38:259-281.
73. Norsys Software Corp Sensitivity to findings. Netica Software Documentation 2006, (www.norsys.com).
74. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988, Morgan Kaufmann.
75. Perry M., Hollis D. The generation of monthly gridded datasets for a range of climatic variables over the UK. Int. J. Climatol. 2005, 25:1041-1054.
76. Pontius R.G., Millones M. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2010, 32:4407-4429.
77. Rawls W.J. Estimating soil bulk-density from particle-size analysis and organic-matter content. Soil Sci. 1983, 135:123-125.
78. Robinson J.W., Hartemink A.J. Learning non-stationary dynamic Bayesian Networks. J. Mach. Learn. Res. 2010, 11:3647-3680.
79. SAGA GIS System for Automated Geoscientific Analysis 2012.
80. Skidmore A.K., Watford F., Luckananurug P., Ryan P.J. An operational GIS expert system for mapping forest soils. Photogramm. Eng. Remote Sens. 1996, 62(5):501-511.
81. Smith C.S., Howes A.L., Price B., McAlpine C.A. Using a Bayesian belief network to predict suitable habitat of an endangered mammal - the Julia Creek dunnart (Sminthopsis douglasi). Biol. Conserv. 2007, 139:333-347.
82. Statistica Statistics and Analytics Software Package 11.0 2012, StatSoft.
83. Steller R.M., Jelinski N.A., Kucharik C.J. Developing models to predict soil bulk density in southern Wisconsin using soil chemical properties. J. Integr. Biosci. 2008, 6(1):53-63.
84. Stewart V.I., Adams W.A., Abdulla H.H. Quantitative pedological studies on soils derived from Silurian mudstones. 2. Relationship between stone content and apparent density of fine earth. J. Soil Sci. 1970, 21:248-255.
85. Suuster E., Ritz C., Roostalu H., Kolli R., Astover A. Modelling soil organic carbon concentration of mineral soils in arable land using legacy soil data. Eur. J. Soil Sci. 2012, 63:351-359.
86. Taalab K.P., Corstanje R., Creamer R., Whelan M.J. Modeling soil bulk density at the landscape scale and its contributions to C stock uncertainty. Biogeosci. Discuss. 2012, 9:18831-18864.
87. Tavares Wahren F., Tarasiuk M., Mykhnovych A., Kit M., Feger K.H., Schwärzel K. Estimation of spatially distributed soil information: dealing with data shortages in the Western Bug Basin, Ukraine. Environ. Earth Sci. 2012, 65:1501-1510.
88. Tranter G., Minasny B., Mcbratney A.B., Murphy B., Mckenzie N.J., Grundy M., Brough D. Building and testing conceptual and empirical models for predicting soil bulk density. Soil Use Manag. 2007, 23:437-443.
89. Uusitalo L. Advantages and challenges of Bayesian Networks in environmental modelling. Ecol. Model. 2007, 203:312-318.
90. Wiesmeier M., Barthold F., Blank B., Koegel-Knabner I. Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 2011, 340:7-24.
91. Zhang H. The Optimality of Naïve Bayes. FLAIRS 2004 Conference. Miami Beach, Florida, USA 2004.
92. Zhao Z., Yang Q., Benoy G., Chow T.L., Xing Z., Rees H.W., Meng F. Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 2010, 90:75-87.