Heatwave Prediction in Switzerland (2025) Context This project explores the prediction of Level 2 heatwave episodes in Switzerland, motivated by the rise in deadly heatwaves (1,444 deaths in France in 2025). The goal is to anticipate these events to inform at-risk populations and activate preventive measures. Methodology Data: Utilization of meteorological data from the Pully station (MeteoSwiss) and the NAO (North Atlantic Oscillation). Feature Engineering: Creation of derived variables (moving averages, humidex, linear trends). Models: Comparison of two approaches: - XGBoost: A decision tree-based model. - LSTM: A recurrent neural network to capture data temporality. Results XGBoost: F1 score of 0.51 (better than random, but overly alarmist). LSTM: F1 score of 0.41 (suffers from *overfitting* due to limited data). Conclusion and Perspectives The results highlight the importance of *feature engineering* and sufficient data for complex models. Improvements are possible by integrating more stations and geopotential levels. 📖 For more details, read the full article on Medium: Link to Medium article](https://www.twine.net/signin
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