AI summary · arXiv: 2208.09079v1 · cs.LG
A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework
The essentials
- Wildfires have become a growing concern due to their impact on the environment, human health, and safety.
- In California alone, wildfires in 2020-2021 burned more acres than the last century combined, causing damages of $148.5 billion during the 2018 wildfire season.
- People living with disabilities are disproportionately affected due to inadequate means of alerts.
- A multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture.
- The system utilizes sensor data from the Environmental Protection Agency and historical wildfire data from 2012 to 2018 compiled into a comprehensive wildfire database - the largest of its kind.
- The novel U-Convolutional-LSTM neural network was designed with a special architecture for extracting key spatial and temporal features from contiguous environmental parameters indicative of impending wildfires.
- This spatio-temporal neural network achieved over 97% accuracy compared to around 76% using traditional convolutional neural networks, successfully predicting five most devastating wildfires in 2018 five to fourteen days in advance.
- A personalized early warning system tailored to individuals with sensory disabilities or respiratory exacerbation conditions was proposed.
- These techniques reveal a lack of social equity in current alert systems as certain populations are disproportionately affected by wildfires due to inadequate means of alerts.
- Future research should focus on addressing these disparities by incorporating more inclusive measures into early warning systems such as those proposed in this study.
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