Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Keywords

Soil texture, Silt, Soil science, Terrain, Landslide

References

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Cited by

• Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, TanzaniaTim Appelhans, Ephraim Mwangomo, Douglas R. Hardy — 2015
• Data-driven methods to improve baseflow prediction of a regional groundwater modelTianfang Xu, Albert J. Valocchi — 2015
• Estimación de la humedad del suelo a niveles de capacidad de campo y punto de marchitez mediante modelos predictivos a escala regionalP. Pérez Cutillas, Gonzalo G. Barberá, Carmelo Conesa García — 2015
• Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystemRen‐Min Yang, Gan‐Lin Zhang, Feng Liu — 2016
• Prediction accuracy of selected spatial interpolation methods for soil texture at farm field scaleDariusz Gozdowskı, Michał Stępień, Stanisław Samborski — 2015
• Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest krigingRaphael A. Viscarra Rossel, R. Webster, Darren Kidd — 2014
• Comparison of three supervised learning methods for digital soil mapping: Application to a complex terrain in the Ecuadorian AndesMartin Hitziger, Mareike Ließ — 2014
• Digital Mapping of Soil Particle‐Size Fractions for NigeriaStephen Akpa, Inakwu Odeh, Thomas F. A. Bishop — 2014
• Comparative performance of classification algorithms for the development of models of spatial distribution of landscape structuresHocine Bourennane, Alain Couturier, Catherine Pasquier — 2014
• Making use of the World Reference Base diagnostic horizons for the systematic description of the soil continuum — Application to the tropical mountain soil-landscape of southern EcuadorMareike Ließ, Bruno Glaser, Bernd Huwe — 2012
• Effective map scales for soil transport processes and related process domains — Statistical and spatial characterization of their scale-specific inaccuraciesMarkus Möller, Martin Völk — 2015
• Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment VariablesShiwen Zhang, Chongyang Shen, Xiaoyang Chen — 2013
• Factors controlling the spatial variability of surface soil particles using GLMs and GAMsDongmei Peng, Fengzhi Shi, Chengyi Zhao — 2014
• Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariatesLaura Poggio, Alessandro Gimona, Mark Brewer — 2013
• Assessment of spatial variability of soil physical and hydraulic properties in Netravati catchment, Karnataka State, IndiaB.P. Ganasri, G. S. Dwarakish — 2015
• Measurement and modelling of the ingestion bioaccessibility of polyaromatic hydrocarbons in soilsMark Cave, Christopher H. Vane, Alexander Kim — 2015
• EVALUATION OF STATISTICAL AND GEOSTATISTICALMODELS OF DIGITAL SOIL PROPERTIES MAPPING INTROPICAL MOUNTAIN REGIONSWaldir de Carvalho, C. da S. Chagas, Philippe Lagacherie — 2014
• Predictive soil parent material mapping at a regional-scale: a Random Forest approach.Brandon Heung, Chuck Bulmer, Margaret Schmidt — 2014
• Digital mapping of soil salinity in Ardakan region, central IranRuhollah Taghizadeh‐Mehrjardi, Budiman Minasny, Fereydoon Sarmadian — 2014
• The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest PredictionsMareike Ließ, Martin Hitziger, Bernd Huwe — 2014

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