Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark

TL;DR

This paper advances wildfire spread prediction by evaluating various data-driven models under controlled conditions, achieving state-of-the-art accuracy with time-series data, and introducing the extensive WSTS+ benchmark with additional historical data.

Contribution

It systematically compares wildfire modeling strategies, identifies the best approaches, and creates the largest public time-series wildfire prediction benchmark, WSTS+.

Findings

Time-series models outperform single-day models in accuracy.

Incorporating more historical data improves prediction performance.

WSTS+ doubles the data scope, enhancing model evaluation.

Abstract

Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict wildfire spread on a given day based upon high-dimensional explanatory data from a single preceding day, or from a time series of T preceding days. For the first time, we investigate a large number of existing data-driven wildfire modeling strategies under controlled conditions, revealing the best modeling strategies and resulting in models that achieve state-of-the-art (SOTA) accuracy for both single-day and multi-day input scenarios, as evaluated on a large public benchmark for next-day wildfire spread, termed the WildfireSpreadTS (WSTS) benchmark. Consistent with prior work, we found that models using time-series input obtained the best overall accuracy, suggesting this is an important future area of research. Furthermore, we create a new benchmark, WSTS+, by incorporating four additional years of historical wildfire data into the WSTS benchmark. Our benchmark doubles the number of unique years of historical data, expands its geographic scope, and, to our knowledge, represents the largest public benchmark for time-series-based wildfire spread prediction.