<ul><li>The Squeeze-and-Excitation Network (SENet) was implemented to address the issue of treating all input features equally in traditional models.</li><li>The SENet adaptively recalibrates feature channels based on relevance, leading to high accuracy and interpretability in wildfire risk assessment.</li><li>Using a simulated wildfire dataset, the SENet achieved an accuracy of 95.8% with precise and recall for fire events.</li><li>Feature importance analysis highlighted the significance of temperature, pressure, and solar radiation in wildfire prediction.</li></ul>

Let the Network Decide: The Channel-Wise Wisdom of Squeeze-and-Excitation Networks

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