Wildfire spots analysis and forecasting: Evaluation of univariate and multivariate based on variational mode decomposition models

Abstract

Every year, forest fires affect large areas of Santa Catarina State in Brazil, causing significant environmental, economic, and property damage. This study aims to predict fire outbreaks in Lages and Xanxerê, municipalities with high incidence rates, using data from the Santa Catarina Military Fire Department (CBMSC). The goal is to support more efficient resource management and personnel allocation during high-risk periods. The time series comprises 70 monthly observations, covering February 2019 to December 2024. The methodology combines univariate and multivariate models with Variational Mode Decomposition (VMD) to generate short-term forecasts for one-, two-, and three-month horizons, applying a rolling-window cross-validation protocol. The data were tested in their original form and with logarithmic and square root transformations to enhance predictive performance. Evaluation measures included RMSE, MAE, sMAPE, and error standard deviation. In Lages, the best result was achieved using square-root transformed data and the VMD–LASSO model (RMSE = 1.2074; sMAPE = 58.90). In Xanxerê, the best performance was achieved by applying the same model to the original data (RMSE = 1.5248; sMAPE = 67.79). These findings provide a decision-support tool for CBMSC, enabling more accurate planning and resource mobilization during critical periods and strengthening wildfire prevention and response strategies.