Multivariate density model comparison for multi-site flood-risk rainfall in the French Mediterranean area

International audienceThe French Mediterranean area is subject to intense rainfall events which might cause flash floods, the main natural hazard in the area. Flood-risk rainfall is defined as rainfall with a high spatial average and encompasses rainfall which might lead to flash floods. We aim to compare eight multivariate density models for multi-site flood-risk rainfall. In particular, an accurate characterization of the spatial variability of flood-risk rainfall is crucial to help understand flash flood processes. Daily data from eight rain gauge stations at the Gardon at Anduze, a small Mediterranean catchment, are used in this work. Each multivariate density model is made of a combination of a marginal model and a dependence structure. Two marginal models are considered: the Gamma distribution (parametric) and the Log-Normal mixture (non-parametric). Four dependence structures are included in the comparison: Gaussian, Student t, Skew Normal and Skew t in increasing order of complexity. They possess a representative set of theoretical properties (symmetry/asymmetry and asymptotic dependence/independence). The multivariate models are compared in terms of three types of criteria: (1) separate evaluation of the goodness-of-fit of the margins and of the dependence structures, (2) model selection with a leave-one-out evaluation of the Anderson-Darling and Cramer-Von Mises statistics and (3) comparison in terms of two hydrologically interpretable quantities (return periods of the spatial average and conditional probabilities of exceedances). The key outcome of the comparison is that the Skew Normal with the Log-Normal mixture margins outperform significantly the other models. The asymmetry introduced by the Skew Normal is an added-value with respect to the Gaussian. Therefore, the Gaussian dependence structure, although widely used in the literature, is not recommended for the data in this study. In contrast, the asymptotically dependent models did not provide a significant improvement over the asymptotically independent ones

Estimation de densité conditionnelle lorsque l'hypothèse de normalité est insatisfaisante

We aim at modelling fat-tailed densities whose distributions are unknown but are potentially asymmetric. In this context, the standard normality assumption is not appropriate.In order to make as few distributional assumptions as possible, we use a non-parametric algorithm to model the center of the distribution. Density modelling becomes more difficult as we move further in the tail of the distribution since very few observations fall in the upper tail area. Hence we decide to use the generalized Pareto distribution (GPD) to model the tails of the distribution. The GPD can approximate finite, exponential or subexponential tails. The estimation of the parameters of the GPD is based solely on the extreme observations. An observation is defined as being extreme if it is greater than a given threshold. The main difficulty with GPD modelling is to determine an appropriate threshold. Nous cherchons à modéliser des densités dont la distribution est inconnue mais qui est asymétrique et présente des queues lourdes. Dans ce contexte, l'hypothèse de normalité n'est pas appropriée. Afin de maintenir au minimum le nombre d'hypothèses distributionnelles, nous utilisons une méthode non paramétrique pour modéliser le centre de la distribution. La modélisation est plus difficile dans les queues de la distribution puisque peu d'observations s'y trouvent. Nous nous proposons donc d'utiliser la Pareto généralisée (GPD) pour modéliser les queues de la distribution. La GPD permet d'approximer tous les types de queues de distributions (qu'elles soient finies, exponentielles ou sous-exponentielles). L'estimation des paramètres de la GPD est uniquement basée sur les observations extrêmes. Une observation est définie comme étant extrême si elle dépasse un seuil donné. La principale difficulté de la modélisation avec la GPD réside dans le choix d'un seuil adéquat.fat-tailed distribution, generalized Pareto, conditional density estimation, distribution à queue épaisse, Pareto généralisée, estimation de densité conditionnelle

Modèles Pareto hybrides pour distributions asymétriques et à queues lourdes

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment

International audienceWe present two statistical models for downscaling flood hazard indicators derived from upscaledshallow water simulations. These downscaling models are based on the decomposition of hazardindicators into linear combinations of spatial patterns obtained from a Principal ComponentAnalysis (PCA). Artificial Neural Networks (ANNs) are used to model the relationship betweenlow resolution (LR) and high resolution (HR) information drawn from hazard indicators. Inboth statistical models, the PCA features, i.e. the linear weights of the spatial patterns, of theLR hazard indicator are taken as inputs to the ANNs. In the first model, there is one ANNper HR cell where the hazard indicator is to be estimated and the output of the ANN is thehazard indicator value at that cell. In the second model, there is a single ANN for the wholeHR mesh whose outputs are the PCA features of the HR hazard indicator. An estimate of thehazard indicator is obtained by combining the ANN’s outputs with the HR spatial patterns.The two statistical downscaling models are evaluated and compared at estimating the waterdepth and the norm of the unit discharge, two common hazard indicators, on simulations fromfive synthetic urban configurations and one field-test case. Analyses are carried out in termsof relative absolute errors of the statistical downscaling model with respect to the LR hazardindicator. They show that (i) both statistical downscaling models provide estimates that aremore accurate than the LR hazard indicator in most cases and (ii) the second downscalingmodel yields consistently lower errors for both hazard indicators for all flow scenarios on allconfigurations considered. The statistical models are three orders of magnitude faster than HRflow simulations. Used in conjunction with upscaled flood models such as porosity models, theyappear as a promising operational alternative to direct flood hazard assessment from HR flowsimulations

Effects of reflux laryngitis on laryngeal chemoreflexes in newborn lambs

It has been suggested that reflux laryngitis (RL) is involved in apneas-bradycardias of the newborn. The aim of the present study was to develop a unique RL model in newborn lambs to test the hypothesis that RL enhances the cardiorespiratory components of the laryngeal chemoreflexes (LCR) in the neonatal period. Gastric juice surrogate (2 ml of normal saline solution with HCl pH 2 + pepsin 300 U/ml) (RL group, n = 6) or normal saline (control group, n = 6) was repeatedly injected onto the posterior aspect of the larynx, 3 times a day for 6 consecutive days, via a retrograde catheter introduced into the cervical esophagus. Lambs instilled with gastric juice surrogate presented clinical signs of RL, as well as moderate laryngitis on histological observation. Laryngeal chemoreflexes were thereafter induced during sleep by injection of 0.5 ml of HCl (pH 2), ewe's milk, distilled water or saline into the laryngeal vestibule via a chronic, transcutaneous supraglottal catheter. Overall, RL led to a significantly greater respiratory inhibition compared with the control group during LCR, including longer apnea duration (P = 0.01), lower minimal respiratory rate (P = 0.002), and a more prominent decrease in arterial hemoglobin saturation (SpO(2)) (P = 0.03). No effects were observed on cardiac variables. In conclusion, 1) our unique neonatal ovine model presents clinical and histological characteristics of RL; and 2) the presence of RL in newborn lambs increases the respiratory inhibition observed with LCR, at times leading to severe apneas and desaturations

Circulating steroid levels as correlates of adipose tissue phenotype in premenopausal women

Background: Obesity-related alterations in the circulating steroid hormone profile remain equivocal in women. Our objective was to identify circulating steroid levels that relate to increased adiposity and altered adipose phenotype in premenopausal women. Materials and methods: In a sample of 42 premenopausal women (age 46±3 years; BMI 27.1±4.2 kg/m2 ), 19 plasma steroids were quantified by ESI-LC-MS/MS. Body composition and fat distribution were assessed by dual-energy X-ray absorptiometry and computed tomography, respectively. Markers of adipose tissue function including adipocyte size distributions, radiological attenuation, and macrophage infiltration were also analyzed in surgically obtained visceral and subcutaneous fat samples. Results: Many negative correlations were observed between adiposity measurements such as BMI, body fat percentage or total abdominal adipose tissue area and plasma levels of androstenedione (r=-0.33 to -0.39, p≤0.04), androsterone (r=-0.30 to -0.38, p≤0.05) and plasma levels of steroid precursor pregnenolone (r=-0.36 to -0.46, p≤0.02). Visceral adipocyte hypertrophy was observed in patients with low pregnenolone concentrations (p<0.05). Visceral adipose tissue radiologic attenuation, a potential marker of adipocyte size, was also positively correlated with pregnenolone levels (r=0.33, p<0.05). Low levels of pregnenolone were related to increased number of macrophages infiltrating visceral and subcutaneous adipose tissue (p<0.05). Conclusion: Plasma levels of androgens and their precursors are lower in women with increased adiposity and visceral adipocyte hypertrophy. Low circulating pregnenolone concentration may represent a marker of adipose tissue dysfunction

Les cahiers de l'IRIPI 4 - Comment aborder les sujets sensibles en classe?

Actes du colloque virtuel tenu les 21 et 22 janvier 2021, publiés sous la direction de Habib El-Hage, directeur de l'IRIPI

Differential Effects of Bartonella henselae on Human and Feline Macro- and Micro-Vascular Endothelial Cells

Bartonella henselae, a zoonotic agent, induces tumors of endothelial cells (ECs), namely bacillary angiomatosis and peliosis in immunosuppressed humans but not in cats. In vitro studies on ECs represent to date the only way to explore the interactions between Bartonella henselae and vascular endothelium. However, no comparative study of the interactions between Bartonella henselae and human (incidental host) ECs vs feline (reservoir host) ECs has been carried out because of the absence of any available feline endothelial cell lines

Système de transactions automatiques sur le marché des contrats à terme sur le taux d’intérêt

Rapport de stage (maîtrise en finance mathématique et computationnelle)Numéro de référence interne originel : a1.1 g 85

A spatially adaptive multi-resolution generative algorithm: Application to simulating flood wave propagation

We propose a statistical model suitable for large spatio-temporal data sets exhibiting complex patterns such as simulated by physics-based hydraulic models over high resolution (HR) 2D meshes. Although necessary for impact studies such as urban flood hazard assessment, their long computation times limit their applicability leading to the development of statistical models that may emulate them quickly. Our model draws from the strengths of multi-resolution analysis and relies on an extension of the lifting scheme, a flexible implementation of discrete wavelet transforms, for spatio-temporal data. The extended lifting scheme exploits the idea that dominant spatial features, that may be identified with clustering, remain present through time. An easily interpretable non-parametric representation can be derived from the lifting scheme by combining a smoothed version of the data (obtained by simple averaging) with details (given by local regression residuals). A generative algorithm is built by introducing the information provided by a low resolution model, whose computation times are orders of magnitude smaller, yielding a downscaling model. This downscaling model assumes that sufficiently representative HR spatial patterns can be inferred from the training set. Our model is applied to a 2D dam break experiment using a synthetic urban configuration and to a field-scale test case simulating the propagation of a dike break flood wave into a Sacramento urban area. A comparison, carried out with spatial interpolation schemes and with a variant of our model based on principal component analysis, shows that the spatio-temporal lifting scheme based model is better at reproducing extreme events