A Semantic Framework for Decision Making in Forest Fire Emergencies

Abstract

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the available information in order to extract useful insights and knowledge to facilitate advanced whereas and support human operators and authorities in a real operational scenario is a challenge. In this work, we present ONTO-SAFE, an ontology-based framework for wildfire events, adopting Semantic Web technologies for data integration and infusion of domain and background knowledge. More specifically, the framework creates a unified representation of the available assets, taking into account data generated from different sources, such as sensors, weather forecasts, earth observations, etc. To this end, previously existing ontologies and standards are used, such as Empathi and EmergencyFire ontology, to provide the conceptual model and the necessary level of abstraction in the form of interconnected knowledge graphs to satisfy the modeling requirements. On top of the generated knowledge graphs, a declarative framework extracts facts and higher-level inferred knowledge from asserted data to support users in decision making. In addition, the framework supports the generation of recommendations, such as sharing important wildfire information with citizens and professionals, that can be adjusted based on user-defined factors and the current disaster risk management phase.