Optimal CNN depth for no-fire confidence

Background: Deep learning models for wildfire prediction, such as CNNs and ConvLSTMs, are often evaluated using standard machine learning metrics computed on curated test datasets.

Question / Future Work: Further investigation is needed to determine the optimal network depth for CNN architectures to achieve the best balance between correctly identifying fire events (high recall) and confidently rejecting false positive events (high confidence in flagging no-fire regions).

Why It Matters: Identifying the optimal complexity (depth) is crucial for engineering the most operationally useful model, where correctly classifying ‘no-fire’ areas with high confidence (low noise) is as important as detecting actual fires.

Evidence: This demonstrates that Deeper CNN 1 has the optimal network depth for confidently flagging the no-fire events. Secondly, while the three CNN architectures show comparable performance in identifying fire events (as reflected in the recall reported in Table 2, and further confirmed in Table 3), the confident identification of no-fire events requires capturing more subtle and complex correlations in the features. This likely explains why the Deeper CNN 1 is more confident in flagging no-fire events than the Basic CNN network.