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Theses

Prévision de la turbidité par apprentissage statistique : application au captage AEP d'Yport (Normandie)

Abstract : Approximately 25% of the world's population is supplied by water from karstic aquifers. The understanding and protection of these appears to be essential in the context of drinking water needs increasing. In addition, contamination of drinking water by turbid water can be highly damaging by resulting in possible contamination of the served populations. In the case of Normandy, regular drinking water cut-off are necessary to preserve the health of the inhabitants. The modeling and prediction of turbidity event appears as a challenging work because of the number of phenomenon and parameters involves in turbidity variation as well as the non-linearity of the link between rainfall and turbidity. Actually, few models have been proposed to represent the relationship between turbidity and rainfall. In this context, by focusing on Yport's pumping well which is responsible for Half of Le Havre city drinking water supply, we propose an application of neural networks for turbidity prediction. During this thesis work, we emphasized the need to carry out sampling campaigns for phytosanitary products to enable the identification of possible phytosanitary product proxies such as turbidity, rainfall or conductivity. Subsequently, the work carried out in this thesis enabled us to (i) designed neural network models allow to predict at 12h and 24h the turbidity variations, (ii) test several ways to improve these models, (iii) integrate multiresolution analysis into neural networks models and finally (iiii) identify a semi proxy for phytosanitary product contamination.
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Michael Savary. Prévision de la turbidité par apprentissage statistique : application au captage AEP d'Yport (Normandie). Sciences de la Terre. Normandie Université, 2018. Français. ⟨NNT : 2018NORMR091⟩. ⟨tel-01992886⟩

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