another significant challenge. It involves incorporating data on weather conditions, topography, fuel moisture, and other relevant factors. Developing advanced machine learning algorithms that can analyze this extensive data and provide accurate real-time predictions is critical for proactive firefighting ...
We conducted a face-to-face and anonymous two-round questionnaire process with the regional firefighting “Bomberos de Navarra” chiefs, focusing on the most experienced in WUI fire suppression in central Navarra. Fire intensity is the main causative factor of home loss given that a fire reaches ...
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The human suppression effect is particularly significant in WUI areas given the high prioritization of protecting people via firefighting activities. Currently, such a suppression effect is implicitly learned by the model from observed fire progression. More explicit model input features associated with ...