<ul><li>Despite LSTM’s potential, accurately predicting dynamic wildfire indices remains challenging due to the complex and volatile nature of environmental factors influencing wildfire risk.</li><li>Using synthetic time-series data, an LSTM model was developed and trained to predict a wildfire risk index based on weather-related features. Cross-validation and hyperparameter tuning were employed to optimize model performance.</li><li>The trained model demonstrated underfitting, producing near-constant predictions that failed to reflect the true variability in the data. There was a discrepancy between actual and predicted values, and the loss values were stable but suboptimal.</li><li>To improve the model's ability to capture patterns in complex wildfire prediction, enhancements in model complexity, feature engineering, and data preprocessing are recommended.</li></ul>

Unlocking the Power of Long Short-Term Memory (LSTM) with Time-Series Sequences

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