Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

Abstract Machine learning algorithms have become very popular for spatial mapping of the
environment due to their ability to fit nonlinear and complex relationships. However, this …

Abstract Machine learning algorithms find frequent application in spatial prediction of biotic
and abiotic environmental variables. However, the characteristics of spatial data, especially …

Aim Global‐scale maps of the environment are an important source of information for
researchers and decision makers. Often, these maps are created by training machine …

This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to
create a transition index map, which then guides the spatial allocation for the extrapolation …

Environmental epidemiology studies require models of multiple exposures to adjust for co-
exposure and explore interactions. We estimated spatiotemporal exposure to surface air …

Spatial and spatiotemporal machine-learning models require a suitable framework for their
model assessment, model selection, and hyperparameter tuning, in order to avoid error …

The radioactive gas radon (Rn) is considered as an indoor air pollutant due to its detrimental
effects on human health. In fact, exposure to Rn belongs to the most important causes for …

Protecting the future of forests relies on our ability to observe changes in forest health. Thus,
developing tools for sensing diseases in a timely fashion is critical for managing threats at …

Clean water is key for sustainable development. However, large gaps in monitoring data
limit our understanding of global hotspots of poor water quality and their evolution over time …