Covariance table: A fast automatic spatial continuity mapping

Computers & Geosciences - Tập 130 - Trang 94-104 - 2019
Jonas Kloeckner1, Péricles Lopes Machado1, Áttila Leães Rodrigues1, João Felipe Coimbra Leite Costa1
1PPGE3M, Mining Engineering Department, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Agronomia, CEP: 91501-970, Porto Alegre, RS, Brazil

Tài liệu tham khảo

Araújo, 2007, Ensemble forecasting of species distributions, Trends Ecol. Evol., 22, 42, 10.1016/j.tree.2006.09.010

Bracewell, 1986

Carvalho, A.R., Carvalho, J.C., Shiguemori, E.H., Da Silva, J.D.S., Ramos, F.M., 2007. Neural network based models for the retrieval of methane concentration vertical profiles from remote sensing data. In: Anais XIII Simposio Brasileiro de Sensoriamento Remoto. Florianopolis, Brasil, vol. 21, p. 6.

Chu, 1993

Deutsch, 1998

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

Isaaks, 1992

Isaaks, 1989

Johnson, 2008, Implementing FFTs in practice

Kohavi, 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, 1137

Marcotte, 1996, Fast variogram computation with FFT, Comput. Geosci., 22, 1175, 10.1016/S0098-3004(96)00026-X

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

Öztopal, 2006, Artificial neural network approach to spatial estimation of wind velocity data, Energy Convers. Manage., 47, 395, 10.1016/j.enconman.2005.05.009

Pardo-Iguzquiza, 1993, The Fourier integral method: an efficient spectral method for simulation of random fields, Math. Geol., 25, 177, 10.1007/BF00893272

Pyrcz, 2006, Semivariogram models based on geometric offsets, Math. Geol., 38, 475, 10.1007/s11004-005-9025-5

Pyrcz, 2006, Spectral corrected semivariogram models, Math. Geol., 38, 891, 10.1007/s11004-006-9053-9

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

Rigol, 2001, Artificial neural networks as a tool for spatial interpolation, Int. J. Geogr. Inf. Sci., 15, 323, 10.1080/13658810110038951

Rizzo, 1994, Characterization of aquifer properties using artificial neural networks: Neural kriging, Water Resour. Res., 30, 483, 10.1029/93WR02477

Tadić, 2015, Examination of geostatistical and machine-learning techniques as interpolators in anisotropic atmospheric environments, Atmos. Environ., 111, 28, 10.1016/j.atmosenv.2015.03.063

Yao, 2004, Reproduction of the mean, variance, and variogram model in spectral simulation, Math. Geol., 36, 487, 10.1023/B:MATG.0000029301.22150.28

Yao, 1998, Automatic modeling of (cross) covariance tables using fast Fourier transform, Math. Geol., 30, 589, 10.1023/A:1022335100486

Yoo, 2001

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Computers & Geosciences

Tập 130

94-104

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