Assessment of the susceptibility of roads to flooding based on geographical information – test in a flash flood prone area (the Gard region, France)

International audienceIn flash flood prone areas, roads are often the first assets affected by inundations which make rescue operations difficult and represent a major threat to lives: almost half of the victims are car passengers trapped by floods. In the past years, the Gard region (France) road management services have realized an extensive inventory of the known road sub- mersions that occurred during the last 40 years. This inven- tory provided an unique opportunity to analyse the causes of road flooding in an area frequently affected by severe flash floods. It will be used to develop a road submersion suscep- tibility rating method, representing the first element of a road warning system.This paper presents the results of the analysis of this data set. A companion paper will show how the proposed road susceptibility rating method can be combined with dis- tributed rainfall-runoff simulations to provide accurate road submersion risk maps.The very low correlation between the various possible ex- planatory factors and the susceptibility to flooding measured by the number of past observed submersions implied the use of particular statistical analysis methods based on the general principals of the discriminant analysis.The analysis led to the definition of four susceptibility classes for river crossing road sections. Validation tests con- firmed that this classification is robust, at least in the con- sidered area. One major outcome of the analysis is that the susceptibility to flooding is rather linked to the location of the road sections than to the size of the river crossing structure (bridge or culvert)

Time series prediction via aggregation : an oracle bound including numerical cost

We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In this direction we present a fairly general result which can be seen as an oracle inequality including the numerical cost of the predictor computation. The numerical cost appears by letting the oracle inequality depend on the number of simulations required in the Monte Carlo approximation. Some numerical experiments are then carried out to support our findings

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models

This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a very simple problem that enables us to clearly show and discuss the main features of the proposed technique. As a practical application, we have also considered the popular (and challenging) problem of estimating the rate parameters of stochastic kinetic models (SKM). SKMs are highly multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.Comment: 35 pages, 8 figure

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

Oral Condition and Incident Coronary Heart Disease: A Clustering Analysis

Poor oral health has been linked to coronary heart disease (CHD). Clustering clinical oral conditions routinely recorded in adults may identify their CHD risk profile. Participants from the Paris Prospective Study 3 received, between 2008 and 2012, a baseline routine full-mouth clinical examination and an extensive physical examination and were thereafter followed up every 2 y until September 2020. Three axes defined oral health conditions: 1) healthy, missing, filled, and decayed teeth; 2) masticatory capacity denoted by functional masticatory units; and 3) gingival inflammation and dental plaque. Hierarchical cluster analysis was performed with multivariate Cox proportional hazards regression models and adjusted for age, sex, smoking, body mass index, education, deprivation (EPICES score; Evaluation of Deprivation and Inequalities in Health Examination Centres), hypertension, type 2 diabetes, LDL and HDL serum cholesterol (low- and high-density lipoprotein), triglycerides, lipid-lowering medications, NT-proBNP and IL-6 serum level. A sample of 5,294 participants (age, 50 to 75 y; 37.10% women) were included in the study. Cluster analysis identified 3,688 (69.66%) participants with optimal oral health and preserved masticatory capacity (cluster 1), 1,356 (25.61%) with moderate oral health and moderately impaired masticatory capacity (cluster 2), and 250 (4.72%) with poor oral health and severely impaired masticatory capacity (cluster 3). After a median follow-up of 8.32 y (interquartile range, 8.00 to 10.05), 128 nonfatal incident CHD events occurred. As compared with cluster 1, the risk of CHD progressively increased from cluster 2 (hazard ratio, 1.45; 95% CI, 0.98 to 2.15) to cluster 3 (hazard ratio, 2.47; 95% CI, 1.34 to 4.57; P < 0.05 for trend). To conclude, middle-aged individuals with poor oral health and severely impaired masticatory capacity have more than twice the risk of incident CHD than those with optimal oral health and preserved masticatory capacity (ClinicalTrials.gov NCT00741728)

Contextual effects of immigrant presence on populist radical right support: testing the ‘halo effect’ on Front National voting in France

This paper examines the relationship between immigration and populist radical right (PRR) support, based on an analysis of the contextual effects of immigrant presence on Front National vote in France in 2017. Using a unique set of survey data geolocalising respondents at the subcommunal level, it finds evidence for the existence of a curvilinear “halo effect,” with substantial increases in the probability of PRR vote in areas surrounding communities with significantly higher-than-average immigrant populations, and independent of other socio-economic context, as well as individual socio-demographic characteristics. Most importantly, a path analysis confirms the presence of individual attitudinal mediators of this halo effect on PRR vote, thus testing the foundation of the halo, namely that the contextual effects of immigrant presence act on attitudes which drive PRR support. These findings provide a significant step forward in understanding the mechanisms linking subjective experience of immigration with voting for the populist radical right

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Bayesian Computation with Intractable Likelihoods

This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments in pseudo-marginal methods, approximate Bayesian computation (ABC), the exchange algorithm, thermodynamic integration, and composite likelihood, paying particular attention to advancements in scalability for large datasets. We also mention R and MATLAB source code for implementations of these algorithms, where they are available.Comment: arXiv admin note: text overlap with arXiv:1503.0806