Wildfire danger prediction optimization with transfer learning
Spiros Maggioros Nikos Tsalkitzis
Convolutional Neural Networks (CNNs) have proven instrumental across various
computer science domains, enabling advancements in object detection,
classification, and anomaly detection. This paper explores the application of
CNNs to analyze geospatial data specifically for identifying wildfire-affected
areas. Leveraging transfer learning techniques, we fine-tuned CNN
hyperparameters and integrated the Canadian Fire Weather Index (FWI) to assess
moisture conditions. The study establishes a methodology for computing wildfire
risk levels on a scale of 0 to 5, dynamically linked to weather patterns.
Notably, through the integration of transfer learning, the CNN model achieved
an impressive accuracy of 95\% in identifying burnt areas. This research sheds
light on the inner workings of CNNs and their practical, real-time utility in
predicting and mitigating wildfires. By combining transfer learning and CNNs,
this study contributes a robust approach to assess burnt areas, facilitating
timely interventions and preventative measures against conflagrations.