Scholarlink

Forests sustain ecology, but wildfires are a significant threat. Fire weather indices help assess hazards but require constant real-time processing. To address this, we developed a Semantic Sensor Network (SSN) ontology model using data from Monesterial Natural Park, enhanced with Semantic Web Rules Language (SWRL). The system integrates Large Language Models (LLMs) and Complex Event Processing (CEP) engines for real-time fire detection. Sensor networks collect climate data (humidity, temperature, wind speed, etc.), which is processed via Spark Streaming and CEP to identify fire-related events. LLMs analyze detected events, while SPARQL queries retrieve relevant insights from the ontology. The results are combined to estimate the overall risk, allowing informed decision-making within a comprehensive Decision Support System (DSS) framework. It makes it easier to understand and deal with the risks of wildfires, as shown by tests that use ontology metrics, query-based testing, event alerts, and LLM performance (F1 score, precision, and recall).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ontology-Based Forest Fire Management Using Complex Event Processing and Large Language Models

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Ritesh Chandra,
  • Sonali Agarwal,
  • Sadhana Tiwari

摘要

Forests sustain ecology, but wildfires are a significant threat. Fire weather indices help assess hazards but require constant real-time processing. To address this, we developed a Semantic Sensor Network (SSN) ontology model using data from Monesterial Natural Park, enhanced with Semantic Web Rules Language (SWRL). The system integrates Large Language Models (LLMs) and Complex Event Processing (CEP) engines for real-time fire detection. Sensor networks collect climate data (humidity, temperature, wind speed, etc.), which is processed via Spark Streaming and CEP to identify fire-related events. LLMs analyze detected events, while SPARQL queries retrieve relevant insights from the ontology. The results are combined to estimate the overall risk, allowing informed decision-making within a comprehensive Decision Support System (DSS) framework. It makes it easier to understand and deal with the risks of wildfires, as shown by tests that use ontology metrics, query-based testing, event alerts, and LLM performance (F1 score, precision, and recall).