Fire Occurrence

Fire occurrence research focuses on improving prediction and detection capabilities, primarily to mitigate risks and damages associated with wildfires and other fire incidents. Current research emphasizes the development and application of advanced machine learning models, including deep learning architectures like convolutional neural networks (CNNs) and transformers, often integrated with explainable AI (XAI) techniques for enhanced interpretability and trust. These efforts aim to improve the accuracy and efficiency of fire detection systems, enhance wildfire risk assessment, and facilitate better resource allocation for prevention and response. The ultimate goal is to reduce the devastating impacts of fire on human society and the environment.