From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors

TL;DR

This paper develops static and dynamic models to assess wildfire risk in California using environmental data, aiming to improve prediction accuracy and inform prevention strategies, with validation across California and Washington.

Contribution

Introduces novel static and dynamic wildfire risk prediction models utilizing diverse environmental factors and validates their effectiveness across different regions.

Findings

Models accurately identify high-risk wildfire areas.

Risk assessment generalizes from California to Washington.

Counterfactual analysis suggests effective risk reduction methods.

Abstract

Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus on a better understanding of the impacts of different factors so as to inform preventive actions. To validate our models and findings, we divide the land of California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and longitude, and compute the risk of each grid based on spatial and temporal conditions. To verify the generalizability of our models, we further expand the scope of wildfire risk assessment from California to Washington without any fine tuning. By performing counterfactual analysis, we uncover the effects of several possible methods on reducing the number of high risk wildfires. Taken together, our study has the potential to estimate, monitor, and reduce the risks of wildfires across diverse areas provided that such environment data is available.