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 …

In recent years, a worldwide expansion in the frequency of large, uncontrolled, and
catastrophic wildfire events has occurred, creating drastic social, economic, and …

The negative sample selection method is a key issue in studies of using machine learning
approaches to spatially assess natural hazards. Recently, a Repeatedly Random …

The burning issue is a very serious issue nowadays in the forest and industries sector. The
workers are facing the problem and losing valuable life. On the other hand, investors are …

This paper details the approach of the team Kohrrelation in the 2021 Extreme Value
Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the …

From our previous study, we have known that only a small number of literatures have
studied peatlands fire modeling in Indonesia. It is including our recent study on the …

In this paper we consider the application of ensemble classification method, which is called
as the Adaptive Boosting (AdaBoost) method, to predict the occurrences of forest fire. To …

In this paper we consider the application of various machine learning approaches for
prediction of the forest fire occurrence in the peatlands area. Here we consider some …

The island of Borneo has faced seasonal forest fires for decades. This phenomenon is
worsening during dry seasons, especially when droughts are concurrent with the El Niño …

Forest fires are one of the major natural disaster problems of the Mediterranean countries.
Their prevention-effective fighting and especially the local prediction of the forest fire risk …