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。

基于本体论、大数据和 LLMs 的森林火灾管理决策支持系统

Decision support system for Forest fire management using Ontology with  Big Data and LLMs

The forests are significant assets for every country. When it gets destroyed,
it may negatively impact the environment, and forest fire is one of the primary
causes. Fire weather indices are widely used to measure fire danger and are
used to issue bushfire warnings. It can also be used to predict the demand for
emergency management resources. Sensor networks have grown in popularity in
data collection and processing capabilities for a variety of applications in
industries such as medical, environmental monitoring, home automation etc.
Semantic sensor networks can collect various climatic circumstances like wind
speed, temperature, and relative humidity. However, estimating fire weather
indices is challenging due to the various issues involved in processing the
data streams generated by the sensors. Hence, the importance of forest fire
detection has increased day by day. The underlying Semantic Sensor Network
(SSN) ontologies are built to allow developers to create rules for calculating
fire weather indices and also the convert dataset into Resource Description
Framework (RDF). This research describes the various steps involved in
developing rules for calculating fire weather indices. Besides, this work
presents a Web-based mapping interface to help users visualize the changes in
fire weather indices over time. With the help of the inference rule, it
designed a decision support system using the SSN ontology and query on it
through SPARQL. The proposed fire management system acts according to the
situation, supports reasoning and the general semantics of the open-world
followed by all the ontologies

本研究介绍了利用语义传感器网络来计算森林火灾的火险指数以及通过 SPARQL 查询 SSN 本体构建一个基于推理的决策支持系统，帮助用户可视化地监测火险指数变化的步骤。