A Machine-Learning Approach to Predicting Daily Wildfire Expansion Rate

Abstract

Accurate predictions of daily wildfire growth rates are crucial, as extreme wildfires have become increasingly frequent in recent years. The factors which determine wildfire growth rates are complex and depend on numerous meteorological factors, topography, and fuel loads. In this paper, we have built upon previous studies that have mapped daily burned areas at the individual fire level around the globe. We applied several Machine Learning (ML) algorithms including XGBoost, Random Forest, and Multilayer Perceptron to predict daily fire growth rate based on meteorological factors, topography, and fuel loads. Our best model on the entire dataset obtained a 1.15 km2 MAE. The ML model obtained a 90% accuracy when predicting whether a fire’s growth rate will increase or decrease the following day, compared to 61% using a logistic regression. We discuss the central factors that determine wildfire growth rate. To the best of our knowledge, this study is the first to perform such analyses on a global dataset.

Assessing Forest Road Network Suitability in Relation to the Spatial Occurrence of Wildfires in Mediterranean Forest Ecosystems

“…The results from the OLS and GWR models for analyzing relationships between road density and explanatory variables are presented in Table 3. According to the greater adjusted R 2 and lower AICc, it can be concluded that the performance of GWR model is superior to that of the OLS. The outcomes from GWR revealed that the wildfire number had a higher R 2 value (0.61) than wildfire size.…”

“…Wildfire is a widespread and significant environmental issue that affects natural and seminatural ecosystems [1,2]. Among factors affecting the occurrence, severity, and extent of wildfires, roads have been recognized as the most important ones, especially in Mediterranean forest ecosystems [3][4][5].…”

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

“…Compared to traditional machine learning and data mining methods, AutoST-Net displays significant advantages. Traditional models typically predict based on simple statistical relationships between historical data and environmental factors [31,32]. In contrast, AutoST-Net utilizes its deep neural network structure to delve into more complex and subtle patterns of fire spread.…”

“…The second type employs machine learning and data mining methods, such as Random Forest and Logistic Regression, to construct predictive models by analyzing historical fire data and environmental factors [29][30][31][32]. Zheng employed Extreme Learning Machines to compute the probability of fire spots igniting, which were then used by cellular automata to define transition rules, simulating the forest fire spread process [33].…”