  Wildfires are increasingly impacting the environment, human health and
safety. Among the top 20 California wildfires, those in 2020-2021 burned more
acres than the last century combined. California's 2018 wildfire season caused
damages of $148.5 billion. Among millions of impacted people, those living with
disabilities (around 15% of the world population) are disproportionately
impacted due to inadequate means of alerts. In this project, a multi-modal
wildfire prediction and personalized early warning system has been developed
based on an advanced machine learning architecture. Sensor data from the
Environmental Protection Agency and historical wildfire data from 2012 to 2018
have been compiled to establish a comprehensive wildfire database, the largest
of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural
network was designed with a special architecture for extracting key spatial and
temporal features from contiguous environmental parameters indicative of
impending wildfires. Environmental and meteorological factors were incorporated
into the database and classified as leading indicators and trailing indicators,
correlated to risks of wildfire conception and propagation respectively.
Additionally, geological data was used to provide better wildfire risk
assessment. This novel spatio-temporal neural network achieved >97% accuracy
vs. around 76% using traditional convolutional neural networks, successfully
predicting 2018's five most devastating wildfires 5-14 days in advance.
Finally, a personalized early warning system, tailored to individuals with
sensory disabilities or respiratory exacerbation conditions, was proposed. This
technique would enable fire departments to anticipate and prevent wildfires
before they strike and provide early warnings for at-risk individuals for
better preparation, thereby saving lives and reducing economic damages.
