Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

Forests are important dynamic systems which are widely affected by fire worldwide. Due to
the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary …

This research investigates the efficiency of combining the Analytical Network Process (ANP)
and fuzzy logic for developing a fire risk map. Major factors influencing the occurrence of …

Fire in forested areas can be regarded as an environmental disaster which is triggered by
either natural forces or anthropogenic activities. Fires are one of the major hazards in …

Forests are important natural resources having the role of supporting economic activity
which plays a significant role in regulating the climate and the carbon cycle. Forest …

The aim of this research was to produce forest fire susceptibility maps (FFSM) based on
evidential belief function (EBF) and binary logistic regression (BLR) models in the Minudasht …

Forest fires globally cause severe losses in vegetation, soil and habitats and inevitably have
direct and indirect negative environmental impacts such as deforestation, climate change …

Due to the importance of the forest fire susceptibility zonation for proper management of this
environmental hazard, this study presents two different hybrids of artificial neural network …

The present study investigates the effect of climatic parameters, such as air relative humidity
and wind speed, on fire spread propagation indexes in the Malekroud Forest, Iran using the …

A machine learning-based approach is applied to simulate and forecast forest fires in the
Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and …