A Conceptual High Level Multiagent System for Wildfire Management

Wildfires pose significant challenges globally due to their increasing frequency and intensity. This article proposes a comprehensive multiagent system (MAS) for wildfire management, leveraging cutting-edge technologies to enhance various stages of wildfire response. The system integrates diverse data sources, including historical fire incidents, real-time weather data, topographical information, land cover data, and spatial data, to improve the accuracy of wildfire prediction. Data preprocessing and feature engineering are employed to optimize datasets for subsequent modeling. A predictive model incorporating methodologies such as artificial neural networks (ANNs), long short-term memory (LSTM) networks, and agent-based models (ABMs) refines integrated data for precise forecasts. The wildfire detection module deploys uncrewed aerial vehicles (UAVs) with thermal infrared (IR) cameras, Internet of Thing (IoT) sensors, and deep learning-based fire detection models for real-time monitoring and early detection. The wildfire monitoring and tracking agent utilizes UAVs, wireless sensor networks, and deep reinforcement learning (DRL) algorithms to provide real-time insights into fire progression. The wildfire area estimation agent employs machine-learning and clustering algorithms to predict fire spread and estimate burning areas accurately. For evacuation planning, the system integrates ABMs, traffic simulations, and decision support systems (DSSs) to optimize evacuation routes and strategies. The situational awareness (SA) and decision-making support agent consolidate data from various sources, offering geospatial visualizations and strategic recommendations. Finally, the firefighting strategy planner uses predictive models, optimization algorithms, and UAV swarm technology to allocate resources effectively. Despite its conceptual nature, the proposed MAS for wildfire management is validated through extensive referencing of existing research and technological advancements, ensuring a solid theoretical foundation, and demonstrating its potential effectiveness. This integrated approach aims to enhance the efficiency and effectiveness of wildfire management, reducing the ecological and societal impact of wildfires.