Random Forest Spatial Interpolation

High-resolution mapping of daily climate variables by aggregating multiple spatial datasets with the random forest algorithm over the conterminous US

(2019) Hirofumi Hashimoto et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sites

(2019) Pengwei Qiao et al.   ENVIRONMENTAL GEOCHEMISTRY AND HEALTH

Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique

(2019) Xiudi Zhu et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling

(2019) Stefanos Georganos et al.   Geocarto International

Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation

(2019) Fabio Veronesi et al.   ECOLOGICAL INDICATORS

Optimal interpolation methods for farmland soil organic matter in various landforms of a complex topography

(2019) Jun Long et al.   ECOLOGICAL INDICATORS

Sampling design optimization for soil mapping with random forest

(2019) Alexandre M.J-C. Wadoux et al.   GEODERMA

Sparse regression interaction models for spatial prediction of soil properties in 3D

(2018) Milutin Pejović et al.   COMPUTERS & GEOSCIENCES

Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation

(2018) Hanna Meyer et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Spatial modelling with Euclidean distance fields and machine learning

(2018) T. Behrens et al.   EUROPEAN JOURNAL OF SOIL SCIENCE

Statistical and Machine Learning forecasting methods: Concerns and ways forward

(2018) Spyros Makridakis et al.   PLoS One

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

(2018) Marjan Čeh et al.   ISPRS International Journal of Geo-Information

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

(2018) Tomislav Hengl et al.   PeerJ

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(2017) Marvin N. Wright et al.   Journal of Statistical Software

Bilinear surface smoothing for spatial interpolation with optional incorporation of an explanatory variable. Part 2: Application to synthesized and rainfall data

(2016) Nikolaos Malamos et al.   HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES

Optimal Spatial Prediction Using Ensemble Machine Learning

(2016) Molly Margaret Davies et al.  

A machine learning approach to geochemical mapping

(2016) Charlie Kirkwood et al.   JOURNAL OF GEOCHEMICAL EXPLORATION

Spatial downscaling of precipitation using adaptable random forests

(2016) Xiaogang He et al.   WATER RESOURCES RESEARCH

Optimal Spatial Prediction Using Ensemble Machine Learning

(2016) Molly Margaret Davies et al.   International Journal of Biostatistics

Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania

(2015) Tim Appelhans et al.   Spatial Statistics

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions

(2015) Tomislav Hengl et al.   PLoS One

Spatial interpolation methods applied in the environmental sciences: A review

(2014) Jin Li et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Spatial estimation of daily precipitation in regions with complex relief and scarce data using terrain orientation

(2014) Lina Mabel Castro et al.   JOURNAL OF HYDROLOGY

Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution

(2014) Milan Kilibarda et al.   JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES

An Overview of the Global Historical Climatology Network-Daily Database

(2012) Matthew J. Menne et al.   JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY

Application of machine learning methods to spatial interpolation of environmental variables

(2011) Jin Li et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

(2011) Tomislav Hengl et al.   THEORETICAL AND APPLIED CLIMATOLOGY