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

arXiv: https://arxiv.org/abs/2405.11346 · HTML: https://mlsanity.com/paper/2405.11346/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 66 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 5 of 5)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management](https://mlsanity.com/paper/2204.03059/) (2022)
- [Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams](https://mlsanity.com/paper/1411.2186/) (2014)
- [Modelling wildland fire burn severity in California using a spatial Super Learner approach](https://mlsanity.com/paper/2311.16187/) (2023)

## Built on by (most cited first, 6 of 6)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems](https://mlsanity.com/paper/2409.00494/) (2024)
- [Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets](https://mlsanity.com/paper/2504.04292/) (2025)
- [Fuzzy Rule based Intelligent Cardiovascular Disease Prediction using Complex Event Processing](https://mlsanity.com/paper/2409.15372/) (2024)
- [Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room](https://mlsanity.com/paper/2602.08949/) (2026)
- [Ontology-Based Knowledge Modeling and Uncertainty-Aware Outdoor Air Quality Assessment Using Weighted Interval Type-2 Fuzzy Logic](https://mlsanity.com/paper/2603.19683/) (2026)

## Abstract (arXiv)

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.