GIS-Integrated Machine Learning for Wildfire Susceptibility Mapping Under Temperature Increase Scenarios: A Bayesian-Optimized ANN Approach

Wildfires are increasingly shaped by climate change, as rising temperatures alter fire regimes. Accurate prediction of wildfire susceptibility under warming scenarios is essential for effective risk management and land-use planning. This study develops a Geographic Information Systems (GIS)-integrated Artificial Neural Network (ANN) framework, optimized via Bayesian hyperparameter tuning, to predict wildfire susceptibility and assess its spatial response to incremental temperature increases. The proposed methodology integrates multi-source geospatial data (Sentinel-2 NDVI, SRTM terrain models, Copernicus ERA5 reanalysis, and VIIRS active fire observations) within a GIS environment, coupling event-based feature extraction with the Bayesian-optimized ANN, which was trained on climatic, topographic, vegetation, and environmental predictor variables. Model evaluation showed that smaller, balanced training datasets achieved stronger generalization than larger ones. Scenario-based analyses simulated temperature increases of +0.5 °C, +1.0 °C, +1.5 °C, and +2.0 °C, reflecting global climate model projections, with all other variables held constant. Results show that warming intensifies susceptibility in existing fire-prone regions and drives the emergence of new wildfire-prone areas with no historical fire occurrence. Relative to current conditions, newly predicted fire-prone locations increased by approximately 36%, 51%, 85%, and 118% across the respective scenarios, indicating a nonlinear susceptibility response to warming. These findings underscore the value of incorporating climate scenarios into GIS-integrated machine learning-based wildfire susceptibility modeling and offer a spatially explicit framework for proactive mitigation and climate-resilient land-use planning.