Intelligent Models for Dynamic Systems Monitoring: A Case Study on Forest Fires

Abstract

Dynamic systems play a crucial role in various fields, including meteorology, finance, and technology, and are characterized by their complexity and interdependencies. Traditional modeling and prediction methods often struggle to capture the intricate behaviors and evolving patterns of these systems, leading to suboptimal control and prediction outcomes. Forest fires are a prime example of dynamic systems, where interactions among meteorological conditions, fuel types, and topography result in unpredictable and nonlinear fire spread patterns. This issue is particularly critical in regions like Algeria, where recent forest fires have caused significant damage, underscoring the need for advanced predictive and management tools. This work aims to study dynamic systems and propose an intelligent and adaptive model for dynamic forest fire prediction using Deep Neural Networks (DNN) and Cellular Automata (CA). The primary advantage of this system lies in its ability to accurately predict fire ignition points based on meteorological and environmental data and to simulate fire spread across various landscapes with greater precision. This dual-method approach enhances detection and simulation accuracy, reduces response times for authorities, and improves wildfire containment and mitigation efforts