Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches
Authors
Keywords
Auxiliary information, Prediction uncertainty, Kriging with external drift, Quantile regression forest, Spatial data
Journal
Environmental Earth Sciences
Volume 78, Issue 1, Pages -
Publisher
Springer Nature
Online
2019-01-03
DOI
10.1007/s12665-018-8032-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
- (2018) Tomislav Hengl et al. PeerJ
- Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates
- (2017) Divan Vermeulen et al. GEODERMA
- Using quantile regression forest to estimate uncertainty of digital soil mapping products
- (2017) Kévin Vaysse et al. GEODERMA
- Predictive geochemical mapping using environmental correlation
- (2016) John Wilford et al. APPLIED GEOCHEMISTRY
- Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran
- (2016) R. Taghizadeh-Mehrjardi et al. GEODERMA
- Mapping topsoil physical properties at European scale using the LUCAS database
- (2016) Cristiano Ballabio et al. GEODERMA
- A machine learning approach to geochemical mapping
- (2016) Charlie Kirkwood et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Stream sediment geochemistry as a tool for enhancing geological understanding: An overview of new data from south west England
- (2016) Charlie Kirkwood et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Approximating Prediction Uncertainty for Random Forest Regression Models
- (2016) John W. Coulston et al. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
- Prediction of the residual strength of clay using functional networks
- (2016) S.Z. Khan et al. Geoscience Frontiers
- Examination of geostatistical and machine-learning techniques as interpolators in anisotropic atmospheric environments
- (2015) Jovan M. Tadić et al. ATMOSPHERIC ENVIRONMENT
- Extreme Learning Machines for spatial environmental data
- (2015) Michael Leuenberger et al. COMPUTERS & GEOSCIENCES
- Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania
- (2015) Tim Appelhans et al. Spatial Statistics
- Application of machine learning methods to spatial interpolation of environmental variables
- (2011) Jin Li et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database
- (2008) Luis Rodríguez Lado et al. GEODERMA
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now