Summary of Decision Support System For Forest Fire Management Using Ontology with Big Data and Llms, by Ritesh Chandra et al.
Decision support system for Forest fire management using Ontology with Big Data and LLMs
by Ritesh Chandra, Shashi Shekhar Kumar, Rushil Patra, Sonali Agarwal
First submitted to arxiv on: 18 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for early forest fire detection is presented, leveraging Apache Spark and Large Language Models (LLMs) to improve fire risk prediction. Building on previous work on Semantic Sensor Network (SSN) ontologies and Semantic Web Rules Language (SWRL), the authors expand SWRL to enhance a Decision Support System (DSS) for forest fires. The system uses LLMs and Spark framework, implementing real-time alerts tailored to various fire scenarios. Evaluation metrics include ontology metrics, query-based evaluations, LLMs score precision, F1 score, and recall measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forest fires are a major threat to ecological balance, but detecting them early can save lives and resources. This paper uses Big Data processing with Apache Spark to predict forest fire risk based on weather and geography data. The approach combines semantic sensor networks with Large Language Models (LLMs) to create a Decision Support System (DSS). Real-time alerts are sent out using Spark streaming, helping firefighters respond quickly to different types of fires. |
Keywords
» Artificial intelligence » F1 score » Precision » Recall
