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 …

The increasing global concern about wildfires, mostly caused by people, has triggered the
development of human-caused fire occurrence models in many countries. The premise is …

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

The main objective of the present study was to utilize Genetic Algorithms (GA) in order to
obtain the optimal combination of forest fire related variables and apply data mining …

With the increasingly severe damage wreaked by forest fires, their scientific and effective
prevention and control has attracted the attention of countries worldwide. The breakthrough …

Thuan Chau is a serious district affected by forest fire in Vietnam, especially in 2016;
however, no forest fire prediction research has been conducted for this region. Thus …

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on
a provincial scale. Potential driving factors were divided into two groups according to scale …

We used three state-of-the-art machine learning techniques (boosted regression tree,
random forest, and support vector machine) to produce a multi-hazard (MHR) map …

Forest fires are disasters that are common around the world. They pose an ongoing
challenge in scientific and forest management. Predicting forest fires improves the levels of …

Forest fire characteristics and their determinants are important considerations for forest
management. However, the interaction among natural and anthropogenic factors and their …