Application of machine learning methods to spatial interpolation of environmental variables

Environmental Modelling & Software - Tập 26 Số 12 - Trang 1647-1659 - 2011
Jin Li1, Andrew D. Heap1, A. W. Potter1, James Daniell1
1Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia

Tóm tắt

Từ khóa


Tài liệu tham khảo

Araújo, 2007, Ensemble forecasting of species distribution, TREE, 22, 42

Arthur, 2010, Influence of woody vegetation on pollinator densities in oilseed Brassica fields in an Australian temperate landscape, Basic Appl. Ecol., 11, 406, 10.1016/j.baae.2010.05.001

Asli, 1995, Comparison of approaches to spatial estimation in a bivariate context, Math. Geol., 27, 641, 10.1007/BF02093905

Bivand, 2008

Breiman, 1996, Bagging predictors, Mach Learn., 24, 123, 10.1007/BF00058655

Breiman, 2001, Random forests, Mach Learn., 45, 5, 10.1023/A:1010933404324

Breiman, 1984

Collins, 1996, A Comparison of Spatial Interpolation Techniques in Temperature Estimation

Cortes, 1995, Support-vector networks, Mach Learn., 20, 273, 10.1007/BF00994018

Cutler, 2007, Random forests for classification in ecology, Ecography, 88, 2783

Dambolena, 2009, Logarithmic transformations in regression: do you transform back correctly?, Primus, 19, 280, 10.1080/10511970802234976

Diaz-Uriarte, 2006, Gene selection and classification of microarray data using random forest, BMC Bioinform., 7, 1

Drake, 2006, Modelling ecological niches with support vector machines, J. Appl. Ecol., 43, 424, 10.1111/j.1365-2664.2006.01141.x

Ellison, 2004, Bayesian inference in ecology, Ecol. Lett., 7, 509, 10.1111/j.1461-0248.2004.00603.x

Gilardi, 2002

Gilardi, 2000, Local machine learning models for spatial data analysis, J. Geogr. Inf. Decis. Anal., 4, 11

Goovaerts, 1997

Goovaerts, 2000, Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, J. Hydrol., 228, 113, 10.1016/S0022-1694(00)00144-X

Goswami, 2007, Real-time flow forecasting in the absence of quantitative precipitation forecasts: a multi-model approach, J. Hydrol, 334, 125, 10.1016/j.jhydrol.2006.10.002

Gregory, 2001, Testing for forecast consensus, J. Bus Econ. Stat., 19, 34, 10.1198/07350010152472599

Guyon, 2009, Analysis of the KDD Cup 2009: fast scoring on a large Orange customer database, 1

Heap, 2008, Geomorphology of the Australian margin and adjacent seafloor, Aust. J. Earth Sci., 55, 555, 10.1080/08120090801888669

Heap, 2008

Hemer, 2006, The magnitude and frequency of combined flow bed shear stress as a measure of exposure on the Australian continental shelf, Cont Shelf Res., 26, 1258, 10.1016/j.csr.2006.03.011

Hengl, 2007

Hengl, 2007, About regression-kriging: from equations to case studies, Comput. Geosci., 33, 1301, 10.1016/j.cageo.2007.05.001

Hoeting, 1999, Bayesian model averaging: a tutorial, Stat. Sci., 14, 382

Legendre, 1998

Li, 2008

Li, 2011, A review of comparative studies of spatial interpolation methods: performance and impact factors, Ecol. Inform., 228, 10.1016/j.ecoinf.2010.12.003

Li, 2010

Liaw, 2002, Classification and regression by radomForest, R News, 2, 18

Maindonald, 2008

Marmion, 2009, Evaluation of consensus methods in predictive species distribution modelling, Divers. Distrib, 15, 59, 10.1111/j.1472-4642.2008.00491.x

Martínez-Cob, 1996, Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain, J. Hydrol., 174, 19, 10.1016/0022-1694(95)02755-6

Nilsson, 2000, Consensus prediction of membrane protein topology, FEBS Lett., 486, 267, 10.1016/S0014-5793(00)02321-8

Odeh, 1995, Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging, Geoderma, 67, 215, 10.1016/0016-7061(95)00007-B

Okun, 2007, Random forest for gene expression based cancer classification: overlooked issues

Pebesma, 2004, Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683, 10.1016/j.cageo.2004.03.012

Pinheiro, 2000

Pitcher, 2008, Seabed environments, habitats and biological assemblages, 377

Prasad, 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosyst, 9, 181, 10.1007/s10021-005-0054-1

2008

Raftery, 2005, Using Bayesian model averaging to calibrate forecast ensembles, Mon Weather Rev., 133, 1155, 10.1175/MWR2906.1

Schuurmans, 2007, Automatic prediction of high-resolution daily rainfall fields for multiple extents: the potential of operational radar, J. Hydrometeorol, 8, 1204, 10.1175/2007JHM792.1

Shan, 2006, Machine learning of poorly predictable ecological data, Ecol. Modell., 195, 129, 10.1016/j.ecolmodel.2005.11.015

Statnikov, 2008, A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification, BMC Bioinform., 9, 319, 10.1186/1471-2105-9-319

Stein, 1988, Use of soil map delineations to improve (co-)kriging of point data on moisture deficits, Geoderma, 43, 163, 10.1016/0016-7061(88)90041-9

Strobl, 2007, Bias in random forest variable importance measures: illustrations, sources and a solution, BMC Bioinform., 8, 25, 10.1186/1471-2105-8-25

Venables, 2002

Verfaillie, 2006, Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, Cont Shelf Res., 26, 2454, 10.1016/j.csr.2006.07.028

Voltz, 1990, A comparison of kriging, cubic splines and classification for predicting soil properties from sample information, J. Soil Sci., 41, 473, 10.1111/j.1365-2389.1990.tb00080.x

Whiteway, 2007

Yamamoto, 2007, On unbiased backtransform of lognormal kriging estimates, Comput. Geosci., 11, 219, 10.1007/s10596-007-9046-x