Machine Learning-based California Wildfire Risk Prediction and Visualization

Abstract

California continuously ranks as the most wildfire prone state in the United States, with 19 billion dollars in fire damage from 2020 alone. The scarcity of wildfire prediction research specifically for California only increases the state's vulnerability to increasingly frequent and severe wildfires. In this paper, we applied four machine learning models to make wildfire risk predictions from NASA remote sensing data and explored the application of 3D computer graphics software to visualize ordinarily complex geospatial information. From 2D satellite images, we were able to achieve unique 3D visualizations and animations which allowed viewers to easily digest the significance of each respective geospatial dataset over a period of 10 years. Among the four machine learning models, the Multi-Layer Perceptron (MLP) model was the most effective in predicting wildfire risk with an 82.97% accuracy. Although accurate, the imbalance of data between fire and non fire data points impacted the precision of our predictions, warranting further consideration. This imbalance highlights an area for improvement in future research efforts and underscores the need for a more comprehensive and balanced dataset to enhance the precision of our wildfire risk predictions.