The sustainability of life on Earth is under increasing threat due to human-induced climate
change. This perilous change in the Earth's climate is caused by increases in carbon dioxide …

Soil influences human health in a variety of ways, with human health being linked to the
health of the soil. Historically, emphasis has been placed on the negative impacts that soils …

SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution
(250 m cell size) using state-of-the-art machine learning methods to generate the necessary …

In the digital soil mapping (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …

Abstract Machine learning models have been widely used to study the prediction of heating
and cooling loads in residential buildings. However, most of these methods use the default …

Salinization of soils and freshwater resources by natural processes and/or human activities
has become an increasing issue that affects environmental services and socioeconomic …

Abstract Knowledge about the spatial variability of soil aggregate stability indices, soil
organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is …

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe,
Cu, Mn) in western Iran, using environmental covariates and applying two machine learning …

Machine Learning (ML) algorithms have been used as an alternative to conventional and
geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is …

Abstract Machine learning (ML) models are increasingly used to study complex
environmental phenomena with high variability in time and space. In this study, the potential …