Recent wildfire outbreaks around the world have prompted concern that climate change is
increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating …

Abstract Knowledge of the causes of forest fires, and of the main driving factors of ignition, is
an indispensable step towards effective fire prevention policies. This study analyses the …

This paper proposes and validates a novel hybrid artificial intelligent approach, named as
Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire …

Fire occurrence, which results from the presence of an ignition source and the conditions for
a fire to spread, is an essential component of fire risk assessment. In this paper, we present …

The main objectives of this paper are to demonstrate the results of an ensemble learning
method based on prediction results of support vector machine and random forest methods …

This paper provides insight into the use of Machine Learning (ML) models for the
assessment of human-caused wildfire occurrence. It proposes the use of ML within the …

Portugal has the highest density of wildfire ignitions among southern European countries.
The ability to predict the spatial patterns of ignitions constitutes an important tool for …

This study assesses forest-fire susceptibility (FFS) in Fars Province, Iran using three
geographic information system (GIS)-based machine-learning algorithms: boosted …

Abstract Information on the spatial incidence of fire ignition density and burnt area, trends
and drivers of wildfires is vitally important in providing support for environmental and civil …

Forest fires are one of the main factors transforming landscapes and natural environments in
a wide variety of ecosystems. The impacts of fire occur both on a global scale, with …