Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates
In the digital soil mapping (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …
relationship between soil observations and environmental covariates. Generally, the most …
Reductions in deforestation and poverty from decentralized forest management in Nepal
Since the 1980's, decentralized forest management has been promoted as a way to
enhance sustainable forest use and reduce rural poverty. Rural communities manage …
enhance sustainable forest use and reduce rural poverty. Rural communities manage …
Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion
The main purpose was to compare discrimination and reliability of four machine learning
models to create gully erosion susceptibility map (GESM) in a part of Ekbatan Dam Basin …
models to create gully erosion susceptibility map (GESM) in a part of Ekbatan Dam Basin …
Real‐time radar–rain‐gauge merging using spatio‐temporal co‐kriging with external drift in the alpine terrain of Switzerland
IV Sideris, M Gabella, R Erdin… - Quarterly Journal of the …, 2014 - Wiley Online Library
The problem of the optimal combination of rain‐gauge measurements and radar
precipitation estimates has been investigated. A method that attempts to generalize well …
precipitation estimates has been investigated. A method that attempts to generalize well …
The de martonne aridity index in Calabria (Southern Italy)
In this paper, the annual rainfall and temperature values, measured in the period 1951-2016
in a region of southern Italy (Calabria), have been spatially interpolated using deterministic …
in a region of southern Italy (Calabria), have been spatially interpolated using deterministic …
Improve ground-level PM2. 5 concentration mapping using a random forests-based geostatistical approach
Accurate measurements of ground-level PM 2.5 (particulate matter with aerodynamic
diameters equal to or less than 2.5 μm) concentrations are critically important to human and …
diameters equal to or less than 2.5 μm) concentrations are critically important to human and …
[HTML][HTML] PM2. 5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences
Over the past decades, mitigation strategies have been adopted both by federal and state
agencies in the United States (US) to improve air quality. Between 2007 and 2009, the US …
agencies in the United States (US) to improve air quality. Between 2007 and 2009, the US …
Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy)
The spatial distribution of rainfall is paramount for water‐related research such as
hydrological modelling and watershed management. The use of different interpolation …
hydrological modelling and watershed management. The use of different interpolation …
The climate sensitivity of carbon, timber, and species richness covaries with forest age in boreal–temperate North America
Climate change threatens the provisioning of forest ecosystem services and biodiversity
(ESB). The climate sensitivity of ESB may vary with forest development from young to old …
(ESB). The climate sensitivity of ESB may vary with forest development from young to old …
Controlling factors in the variability of soil magnetic measures by machine learning and variable importance analysis
The present work is the first effort to apply machine learning approaches and variable
importance analysis (VIA) to the determination of environmental attributes that regulate the …
importance analysis (VIA) to the determination of environmental attributes that regulate the …