An intelligent cellular automaton scheme for modelling forest fires

Abstract

Forest fires have devastating consequences for the environment, the economy and human lives. Understanding their dynamics is therefore crucial for planning the resources allocated to combat them effectively. In a world where the incidence of such phenomena is increasing every year, the demand for efficient and accurate computational models is becoming increasingly necessary. In this study, we perform a revision of an initial proposal which consists of a two-dimensional propagation model based on cellular automata (2D-CA), which aims to understand the dynamics of these phenomena. We identify the key theoretical weaknesses and propose improvements to address these limitations. We also assess the effectiveness and accuracy of the model by evaluating improvements using real forest fire data (Beneixama, Alicante 2019). Moreover, as a result of the theoretical modifications performed, we introduce a novel intelligent architecture that seeks to capture relationships between system cells from the data. This new architecture has the ability to advance our understanding of forest fire dynamics, contributing to both the evaluation of existing protocols and more efficient firefighting resource management.