Deep learning, which has exhibited considerable potential and effectiveness in forest
resource assessment, is vital for comprehending and managing forest resources and …

Floods are one of the most widespread natural hazards occurring across the globe. The
main objective of this study was to produce flood susceptibility maps for the province of …

Artificial Intelligence (AI) is believed to have a significant potential use in tackling climate
change. This paper explores the connections between AI and climate change research as a …

One of the worst environmental catastrophes that endanger the Australian community is
wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and …

In recent years, the number of wildfires has increased all over the world. Therefore, map**
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …

Predicting and map** fire susceptibility is a top research priority in fire-prone forests
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …

Forest fires represent a critical global threat to both humans and ecosystems. This study
examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using …

Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is
essential for biodiversity conservation, climate change mitigation, and community safety …

This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of
Antalya province in Turkey using different machine learning (ML) techniques. Forest fire …

Wildfires threaten and kill people, destroy urban and rural property, degrade air quality,
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …