FFO: an ontological decision support system for forest fire alert and management services

Abstract

Forest fires, also known as wildfires, present a significant danger to property, human lives, and the natural environment. In order to minimize the impact caused by these emergencies, it is crucial to detect and mitigate them at an early stage. Unfortunately, there are often shortcomings in the detection mechanisms, leading to delayed responses and increased destruction. These detection anomalies can stem from sensor defects or a lack of information interoperability among the sensors deployed in forest areas. To address these challenges, this paper proposes a lightweight ontological framework. The main objective of this framework is to improve forest fire detection and management. One of the key issues in achieving effective interoperability is the heterogeneity in technologies employed and the diverse data generated by different sensors. In response to this problem, the Forest Fire Detection and Management Ontology, FFO, is introduced as a standardized model for sharing and reusing knowledge and data across various sensor systems. To validate the proposed ontology, semantic reasoning, and query processing techniques are utilized. Real-time data collected from experiments conducted in a forest setting are stored as RDF (Resource Description Framework) triples, aligning with the design principles of the ontology.