Zaffran, Mme Margaux (2020) Clustering of rainfall extremes by coupling Kullback-Leibler divergence and machine learning algorithms PFE - Project Graduation, ENSTA.
Climate extremes have a strong societal impact, especially extreme rainfalls. Recent dramatic events, like Alex Storm that stroke northern Italy and South-East of France, emphasize the need of a good knowledge of extreme events to adapt infrastructures. Inherently suffering of too few data, this field would benefit a tool which creates homogeneous regions to improve the extreme studies. We propose a clustering method tailored for extreme and based on the probability distributions. Furthermore, we analyze its results on a daily rainfall data-set from Meteo-France.
|Item Type:||Thesis (PFE - Project Graduation)|
|Uncontrolled Keywords:||clustering, Kullback-Leibler divergence, extreme values, Extreme Value Theory, rainfall, climate|
|Subjects:||Mathematics and Applications|
|Deposited By:||Margaux Zaffran|
|Deposited On:||13 nov. 2020 09:00|
|Dernière modification:||13 nov. 2020 09:00|
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