Forests are crucial for ecological balance, but wildfires, a major cause of
forest loss, pose significant risks. Fire weather indices, which assess
wildfire risk and predict resource demands, are vital. With the rise of sensor
networks in fields like healthcare and environmental monitoring, semantic
sensor networks are increasingly used to gather climatic data such as wind
speed, temperature, and humidity. However, processing these data streams to
determine fire weather indices presents challenges, underscoring the growing
importance of effective forest fire detection. This paper discusses using
Apache Spark for early forest fire detection, enhancing fire risk prediction
with meteorological and geographical data. Building on our previous development
of Semantic Sensor Network (SSN) ontologies and Semantic Web Rules Language
(SWRL) for managing forest fires in Monesterial Natural Park, we expanded SWRL
to improve a Decision Support System (DSS) using a Large Language Models (LLMs)
and Spark framework. We implemented real-time alerts with Spark streaming,
tailored to various fire scenarios, and validated our approach using ontology
metrics, query-based evaluations, LLMs score precision, F1 score, and recall
measures.

利用 Apache Spark 对早期森林火灾进行检测、通过气象和地理数据提高火灾风险预测的论文进行了讨论，使用了 Semantic Sensor Networks、Large Language Models 和 Spark 框架改进了决策支持系统，并通过实时警报和多种火灾场景的验证来衡量方法的有效性。