Adversarial AI Model for Fact-Checking Wildfire Chatbot Answers, Reliability, and Accuracy
Year: 2024
Type: article
Abstract: Wildfires pose a severe threat to both the environment and human health, causing over one billion dollars in infrastructure damage annually. Wildfire smoke contaminates the air with hazardous pollutants such as lead, exacerbating the risk of cardiovascular and respiratory diseases. This study compares the accuracy of our wildfire prediction model with that of the existing WildfireGPT, using actual values from the NASA Fire Radiative Power (FRP) and MODIS MOD14 Wildfire datasets. A comprehensive dataset, including features such as the Fire Weather Index (FWI), Vapor Pressure Deficit (VPD), temperature (T), and pressure (P), was utilized to evaluate the accuracy of our prediction model. The analysis specifically compares the predicted FRP values against observed data to assess the model’s performance. This research aims to significantly impact wildfire prediction by providing a detailed comparative analysis that can guide future improvements. Accurate wildfire prediction is crucial for saving lives and protecting natural habitats. The study highlights the ongoing need to develop and refine predictive technologies, aiming for enhanced accuracy and usability in future applications. It also contributes to a deeper understanding of how improved wildfire prediction can advance management practices.
Authors: Patel, Ayan, Rohit Kamath, Sharif, Nabiha, Lei, Abigail, Ramesh, Meghana Koramutla, Ziheng Sun
Institution: George Mason University
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Life on land
Open Access status: diamond
OpenAlex ID: W7092466762
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