A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework

TL;DR

This paper introduces a novel multi-modal machine learning framework using a U-Convolutional-LSTM neural network for wildfire prediction and a personalized early warning system, significantly improving prediction accuracy and aiding vulnerable populations.

Contribution

The study develops a new spatio-temporal neural network architecture and a comprehensive wildfire database, enabling more accurate predictions and personalized alerts for at-risk individuals.

Findings

Achieved over 97% prediction accuracy for wildfires

Successfully predicted 2018's major wildfires 5-14 days in advance

Enhanced wildfire risk assessment with geological data integration

Abstract

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.