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 …

Wildfires have been systematically studied from the early 1950s, with significant progress in
the applied computational methodologies during the 21st century. However, modern …

Forest fire disaster is currently the subject of intense research worldwide. The development
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …

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 …

The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …

Forest fires may have devastating consequences for the environment and for human lives.
The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer …

Forest fires have caused considerable losses to ecologies, societies, and economies
worldwide. To minimize these losses and reduce forest fires, modeling and predicting the …

The main objective of the present study was to provide a novel methodological approach for
flash flood susceptibility modeling based on a feature selection method (FSM) and tree …

The aim of the current study was to suggest a multi-hazard probability assessment in Fars
Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting …

This paper presents a review of concepts related to wildfire risk assessment, including the
determination of fire ignition and propagation (fire danger), the extent to which fire may …