Abstract

Wildfires are events in which fire spreads uncontrollably, destroying natural ecosystems and causing severe biodiversity loss. Driven by climate change, the frequency and intensity of these events have escalated, making effective risk mitigation a critical global priority. This study proposes a hybrid methodology for the identification and classification of wildfire risk zones, based on the combination of a convolutional neural network (CNN) with U-Net architecture and ResNet-50 backbone for semantic segmentation, together with a fuzzy inference system. The CNN processes high-resolution georeferenced RGB imagery to identify environmental patterns such as vegetation density and combustible organic matter. Evaluated through cross-validation, the CNN achieved a global IoU of 86.73% and a global F1-score of 92.82%, generating a risk classification into low, medium, and high levels. Subsequently, this categorical output is integrated into a fuzzy inference system along with meteorological variables (temperature, humidity, and wind speed). The fuzzy inference system dynamically adjusts the initial risk assessment according to meteorological conditions and generates an updated risk classification for the processed images. This approach significantly improves wildfire risk assessment, providing a data-driven tool for environmental management and early disaster prevention.

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