A novel agent-based model for forest fire prediction

Abstract

In recent years, forest fires have increased drastically due to global warming. Forest fire prediction is the best way to control the spread of fire. Therefore, several studies have focused on developing models that predict the behavior of forest fires. Predicting fire spread and its behavior is crucial to mitigate the adverse effects on weather conditions, environment, and human activities. Improving forest fire prediction using higher quality data can be expensive. In some cases, obtaining or even precise estimation of these data is difficult. On the other hand, using prediction models are more reasonable and feasible to increase prediction accuracy. In this paper, we introduced a novel Belief-Desire-Intention (BDI) agent-based model to predict the behavior of forest fires in the Mazandaran region in the north of Iran. This paper attempted to map the concepts of BDI agent architecture into generic GIS. A novel BDI-GIS model was then proposed in which an agent's belief, desire, and intention were defined based on spatial or non-spatial data and GIS functions. Therefore, an agent-based model was developed to determine the prediction of forest fires and implemented it on a real dataset. The experimental results showed that the proposed model could be successfully applied to the real-world scenarios with a Kappa Coefficient of more than 68.2%.