Spatial prediction of soil properties in temperate mountain regions using support vector regression

Abstract Digital soil mapping in mountain areas faces two major limitations: the small number of available observations and the non-linearity of the relations between environmental variables and soil properties. A possible approach to deal with these limitations involves the use of non-parametric models to interpolate soil properties of interest. Among the different approaches currently available, Support Vector Regression (SVR) seems to have several advantages over other techniques. SVR is a set of techniques in which model complexity is limited by the learning algorithm itself, which prevents overfitting. Moreover, the non-linear approximation of SVR is based on a kernel transformation of the data, which avoids the use of complex functions and is computationally feasible; while the resulting projection in feature space is especially suited for sparse datasets. A brief introduction to this methodology, a comparison with other popular methodologies and a framework for the application of this approach to a study site in the Italian Alps is discussed.

[1]  Thorsten Behrens,et al.  Digital soil mapping using artificial neural networks , 2005 .

[2]  Saturnino Maldonado-Bascón,et al.  Tuning L1-SVM Hyperparameters with Modified Radius Margin Bounds and Simulated Annealing , 2007, IWANN.

[3]  Etienne Barnard,et al.  Data characteristics that determine classifier performance , 2006 .

[4]  Ralph Dubayah,et al.  Topographic Solar Radiation Models for GIS , 1995, Int. J. Geogr. Inf. Sci..

[5]  Toshio Odanaka,et al.  ADAPTIVE CONTROL PROCESSES , 1990 .

[6]  R. Hickey,et al.  Estimating the LS Factor for RUSLE through Iterative Slope Length Processing of Digital Elevation Data within Arclnfo Grid , 2001 .

[7]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[8]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[9]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[10]  Gerard B. M. Heuvelink,et al.  Spacetime Kalman filtering of soil redistribution , 2006 .

[11]  Ward Chesworth,et al.  Encyclopedia of soil science. , 2008 .

[12]  A-Xing Zhu,et al.  Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic , 2001 .

[13]  Willy Tinner,et al.  Vegetation changes and timberline fluctuations in the Central Alps as indicators of holocene climatic oscillations , 1997 .

[14]  V. Vapnik.  Pattern recognition using generalized portrait method , 1963 .

[15]  Xiao-Feng Liao,et al.  A Multiresolution Wavelet Kernel for Support Vector Regression , 2006, ISNN.

[16]  Fausto Guzzetti,et al.  Landslide fatalities and the evaluation of landslide risk in Italy , 2000 .

[17]  Karl W. Steininger,et al.  Global Environmental Change in Alpine Regions , 2002 .

[18]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[19]  Willy Tinner,et al.  Early-Holocene afforestation processes in the lower subalpine belt of the Central Swiss Alps as inferred from macrofossil and pollen records , 2005 .

[20]  Fabio Terribile,et al.  Soil hydraulic behaviour of a selected benchmark soil involved in the landslide of Sarno 1998 , 2003 .

[21]  Philippe Lagacherie,et al.  Digital soil mapping : an introductory perspective , 2007 .

[22]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[23]  T. A. Bancroft,et al.  Research papers in statistics , 1966 .

[24]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[25]  Joachim Diederich,et al.  Eclectic Rule-Extraction from Support Vector Machines , 2005 .

[26]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[27]  N. McKenzie,et al.  Spatial prediction of soil properties using environmental correlation , 1999 .

[28]  Giovanni B. Crosta,et al.  Large sackung along major tectonic features in the Central Italian Alps , 2006 .

[29]  D. Basak,et al.  Support Vector Regression , 2008 .

[30]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

[31]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .

[32]  Joachim Diederich,et al.  Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets , 2004 .

[33]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[34]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[35]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[36]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[37]  Karl W. Steininger,et al.  Global environmental change in Alpine regions : recognition, impact, adaptation and mitigation , 2002 .

[38]  Adrian C. Newton,et al.  Mountain watch : environmental change & sustainable development in mountains , 2002 .

[39]  R. Bellman,et al.  V. Adaptive Control Processes , 1964 .

[40]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[41]  Alex B. McBratney,et al.  Soil pattern recognition with fuzzy-c-means : application to classification and soil-landform interrelationships , 1992 .

[42]  C. Thorne,et al.  Quantitative analysis of land surface topography , 1987 .

[43]  Andreas Zell,et al.  Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression , 2004 .

[44]  R. M. Lark,et al.  Chapter 23 Decomposing Digital Soil Information by Spatial Scale , 2006 .

[45]  A.P.J. de Roo,et al.  Modelling runoff and sediment transport in catchments using GIS , 1998 .