# Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection

arXiv: https://arxiv.org/abs/2411.10108 · HTML: https://mlsanity.com/paper/2411.10108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 73 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [New Probabilistic-Dynamic Multi-Method Ensembles for Optimization based on the CRO-SL](https://mlsanity.com/paper/2212.00742/) (2022)

## Abstract (arXiv)

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.