相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters
Panagiotis Tziachris et al.
CATENA (2019)
Environmental controls on soil pH in planted forest and its response to nitrogen deposition
Songbai Hong et al.
ENVIRONMENTAL RESEARCH (2019)
Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations
Curtis J. Ransom et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)
Regression kriging as a workhorse in the digital soil mapper's toolbox
H. Keskin et al.
GEODERMA (2018)
Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods
M. S. Sirsat et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)
Effect of Soil pH on the Growth, Reproductive Investment and Pollen Allergenicity of Ambrosia artemisiifolia L.
Rodolfo Gentili et al.
FRONTIERS IN PLANT SCIENCE (2018)
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Tomislav Hengl et al.
PEERJ (2018)
Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis
Ahmad Alwosheel et al.
JOURNAL OF CHOICE MODELLING (2018)
Implementation of a sigmoid depth function to describe change of soil pH with depth
Yakun Zhang et al.
GEODERMA (2017)
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C plus plus and R
Marvin N. Wright et al.
JOURNAL OF STATISTICAL SOFTWARE (2017)
Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning
Tomislav Hengl et al.
NUTRIENT CYCLING IN AGROECOSYSTEMS (2017)
Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging
Ying-Qiang Song et al.
SUSTAINABILITY (2017)
Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information
Panagiotis Tziachris et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2017)
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl et al.
PLOS ONE (2017)
A comparative study between popular statistical and machine learning methods for simulating volume of landslides
Ataollah Shirzadi et al.
CATENA (2017)
Spatial variability of soil organic matter using remote sensing data
S. Mirzaee et al.
CATENA (2016)
Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran
Omid Rahmati et al.
CATENA (2016)
An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping
Brandon Heung et al.
GEODERMA (2016)
Altitudinal patterns and controls of plant and soil nutrient concentrations and stoichiometry in subtropical China
Xianjin He et al.
SCIENTIFIC REPORTS (2016)
Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical-mechanical characteristics in wine grapes
Luca Brillante et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2015)
Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania
Tim Appelhans et al.
SPATIAL STATISTICS (2015)
Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
G. Chirici et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2013)
About regression-kriging: From equations to case studies
Tomislav Hengl et al.
COMPUTERS & GEOSCIENCES (2007)
The spatial prediction of soil mineral N and potentially available N using elevation
SJ Baxter et al.
GEODERMA (2005)
The use of backpropagating artificial neural networks in land cover classification
T Kavzoglu et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2003)
Prediction of soil properties by digital terrain modelling
IV Florinsky et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2002)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
A comparison of prediction methods for the creation of field-extent soil property maps
TFA Bishop et al.
GEODERMA (2001)
An overview of pedometric techniques for use in soil survey
AB McBratney et al.
GEODERMA (2000)
分享论文
