Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Spatially continuous data of environmental variables are often required for environmental
sciences and management. However, information for environmental variables is usually …

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is
often carried out on the limited sampling points in the study area to get the overall status of …

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

For many decades, kriging and deterministic interpolation techniques, such as inverse
distance weighting and nearest neighbour interpolation, have been the most popular spatial …

Importance of target-oriented validation strategies for spatio-temporal prediction models is
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …

Big data analytics (BDA) has emerged as a significant area of research for both researchers
and practitioners in the retail industry, indicating the importance and influence of solving …

Current methods of spatial prediction are based on either the First Law of Geography or the
statistical principle or the combination of these two. The Second Law of Geography …

Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

In China, ozone pollution shows an increasing trend and becomes the primary air pollutant
in warm seasons. Leveraging the air quality monitoring network, a random forest model is …