Wildfire Air Quality Prediction: A Data-Driven Approach

Wildfires are extremely harmful to the environment. While producing gaseous pollutants and particles that cause smoke, wildfires also release carbon dioxide (CO2), a greenhouse gas that will continue to warm the planet after the wildfire ends. This article delves into the impact of wildfires and air...

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Bibliographic Details
Published in:International journal of disaster response and emergency management (Print) 2023-09, Vol.6 (1), p.1-22
Main Authors: Dhar, Subhankar, Gao, Jerry Zeyu
Format: Article
Language:English
Subjects:
Air quality
Carbon dioxide
Carbon monoxide
Greenhouse gases
Learning algorithms
Living conditions
Machine learning
Mathematical models
Neural networks
Particulate matter
Performance indices
Public safety
Root-mean-square errors
Wildfires
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Summary:Wildfires are extremely harmful to the environment. While producing gaseous pollutants and particles that cause smoke, wildfires also release carbon dioxide (CO2), a greenhouse gas that will continue to warm the planet after the wildfire ends. This article delves into the impact of wildfires and air quality on human living conditions. The authors' machine learning models use wildfire data to forecast air quality with detailed indexes and geographic information during a wildfire. The work evaluates the performance of each machine learning model via statistical metrics like mean absolute error (MAE), R-squared (R2), and root mean squared error (RMSE). The experimental results used neural networks to predict a specific value for carbon monoxide (CO), ozone, and PM2.5. These are both promising and accurate, providing meaningful insight into air quality within a region. This work will be useful for cities, governments, citizens, and public safety.
ISSN:2572-4940
2572-4932
DOI:10.4018/IJDREM.330148