Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

A limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.

Statistical analysis and modelling of weather radar beam propagation conditions in the Po Valley (Italy)

Ground clutter caused by anomalous propagation (anaprop) can affect seriously radar rain rate estimates, particularly in fully automatic radar processing systems, and, if not filtered, can produce frequent false alarms. A statistical study of anomalous propagation detected from two operational C-band radars in the northern Italian region of Emilia Romagna is discussed, paying particular attention to its diurnal and seasonal variability. The analysis shows a high incidence of anaprop in summer, mainly in the morning and evening, due to the humid and hot summer climate of the Po Valley, particularly in the coastal zone. Thereafter, a comparison between different techniques and datasets to retrieve the vertical profile of the refractive index gradient in the boundary layer is also presented. In particular, their capability to detect anomalous propagation conditions is compared. Furthermore, beam path trajectories are simulated using a multilayer ray-tracing model and the influence of the propagation conditions on the beam trajectory and shape is examined. High resolution radiosounding data are identified as the best available dataset to reproduce accurately the local propagation conditions, while lower resolution standard TEMP data suffers from interpolation degradation and Numerical Weather Prediction model data (Lokal Model) are able to retrieve a tendency to superrefraction but not to detect ducting conditions. Observing the ray tracing of the centre, lower and upper limits of the radar antenna 3-dB half-power main beam lobe it is concluded that ducting layers produce a change in the measured volume and in the power distribution that can lead to an additional error in the reflectivity estimate and, subsequently, in the estimated rainfall rate.

Modelling of weather radar echoes from anomalous propagation using a hybrid parabolic equation method and NWP model data

Contamination of weather radar echoes by anomalous propagation (anaprop) mechanisms remains a serious issue in quality control of radar precipitation estimates. Although significant progress has been made identifying clutter due to anaprop there is no unique method that solves the question of data reliability without removing genuine data. The work described here relates to the development of a software application that uses a numerical weather prediction (NWP) model to obtain the temperature, humidity and pressure fields to calculate the three dimensional structure of the atmospheric refractive index structure, from which a physically based prediction of the incidence of clutter can be made. This technique can be used in conjunction with existing methods for clutter removal by modifying parameters of detectors or filters according to the physical evidence for anomalous propagation conditions. The parabolic equation method (PEM) is a well established technique for solving the equations for beam propagation in a non-uniformly stratified atmosphere, but although intrinsically very efficient, is not sufficiently fast to be practicable for near real-time modelling of clutter over the entire area observed by a typical weather radar. We demonstrate a fast hybrid PEM technique that is capable of providing acceptable results in conjunction with a high-resolution terrain elevation model, using a standard desktop personal computer. We discuss the performance of the method and approaches for the improvement of the model profiles in the lowest levels of the troposphere.

Modelling the risk of nitrate leaching from two soils amended with five different biosolids

High N concentrations in biosolids are one of the strongest reasons for their agricultural use. However, it is essential to understand the fate of N in soils treated with biosolids for both plant nutrition and managing the environmental risk of NO3--N leaching. This work aimed at evaluating the risk of NO3--N leaching from a Spodosol and an Oxisol, each one treated with 0.5-8.0 dry Mg ha-1 of fresh tertiary sewage sludge, composted biosolids, limed biosolids, heat-dried biosolids and solar-irradiated biosolids. Results indicated that under similar application rates NO3--N accumulated up to three times more in the 20 cm topsoil of the Oxisol than the Spodosol. However, a higher water content held at field capacity in the Oxisol compensated for the greater nitrate concentrations. A 20 % NO3--N loss from the root zone in the amended Oxisol could be expected. Depending on the biosolids type, 42 to 76 % of the NO3--N accumulated in the Spodosol could be expected to leach down from the amended 20 cm topsoil. NO3--N expected to leach from the Spodosol ranged from 0.8 (composted sludge) to 3.5 times (limed sludge) the amounts leaching from the Oxisol treated alike. Nevertheless, the risk of NO3--N groundwater contamination as a result of a single biosolids land application at 0.5-8.0 dry Mg ha-1 could be considered low.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Accurate memory for object location by individuals with intellectual disability: Absolute spatial tagging instead of configural processing?

Using head-mounted eye tracker material, we assessed spatial recognition abilities (e.g., reaction to object permutation, removal or replacement with a new object) in participants with intellectual disabilities. The "Intellectual Disabilities (ID)" group (n=40) obtained a score totalling a 93.7% success rate, whereas the "Normal Control" group (n=40) scored 55.6% and took longer to fix their attention on the displaced object. The participants with an intellectual disability thus had a more accurate perception of spatial changes than controls. Interestingly, the ID participants were more reactive to object displacement than to removal of the object. In the specific test of novelty detection, however, the scores were similar, the two groups approaching 100% detection. Analysis of the strategies expressed by the ID group revealed that they engaged in more systematic object checking and were more sensitive than the control group to changes in the structure of the environment. Indeed, during the familiarisation phase, the "ID" group explored the collection of objects more slowly, and fixed their gaze for a longer time upon a significantly lower number of fixation points during visual sweeping.

Spatial and temporal variation of natural toxicity in cnidarians, bryozoans and tunicates in Mediterranean caves

The natural toxicity of cnidarians, bryozoans and tunicates in two caves was assessed using the Microtox® technique in spring and autumn. One cave was located in the Cabrera Archipelago (Balearic Islands) and the other in the Medes Islands (Catalan littoral). The organisms analysed were good representatives of the coverage of each Phylum in the communities; however, these Phyla are less abundant than sponges which are the dominant group in these caves. Seventy-one percent of the species of cnidarians and bryozoans analysed were toxic in one of the caves, communities or seasons, which indicates the relevance of bioactive species in these groups. The tunicate Lissoclinum perforatum was the most toxic species. Although all three Phyla had some highly toxic species, a common pattern that related the caves, communities and seasons was not found. Seasonal variation of toxicity in cnidarians and bryozoans was higher in the Cabrera than in the Medes cave. Moreover, variation in toxicity either between communities or between seasons was a common trait for most cnidarians and bryozoans, whereas tunicates remained toxic throughout communities and seasons.

Silver-assisted laser desorption ionization for high spatial resolution imaging mass spectrometry of olefins from thin tissue sections.

Silver has been demonstrated to be a powerful cationization agent in mass spectrometry (MS) for various olefinic species such as cholesterol and fatty acids. This work explores the utility of metallic silver sputtering on tissue sections for high resolution imaging mass spectrometry (IMS) of olefins by laser desorption ionization (LDI). For this purpose, sputtered silver coating thickness was optimized on an assorted selection of mouse and rat tissues including brain, kidney, liver, and testis. For mouse brain tissue section, the thickness was adjusted to 23 ± 2 nm of silver to prevent ion suppression effects associated with a higher cholesterol and lipid content. On all other tissues, a thickness of at 16 ± 2 nm provided the best desorption/ionization efficiency. Characterization of the species by MS/MS showed a wide variety of olefinic compounds allowing the IMS of different lipid classes including cholesterol, arachidonic acid, docosahexaenoic acid, and triacylglyceride 52:3. A range of spatial resolutions for IMS were investigated from 150 μm down to the high resolution cellular range at 5 μm. The applicability of direct on-tissue silver sputtering to LDI-IMS of cholesterol and other olefinic compounds presents a novel approach to improve the amount of information that can be obtained from tissue sections. This IMS strategy is thus of interest for providing new biological insights on the role of cholesterol and other olefins in physiological pathways or disease.

Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging.

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learning-induced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to approximately 75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted <6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Growth and externalities across economies : an empirical analysis using spatial econometrics

Recent theoretical models of economic growth have emphasised the role of external effects on the accumulation of factors of production. Although most of the literature has considered the externalities across firms within a region, in this paper we go a step further and consider the possibility that these externalities cross the barriers of regional economies. We assess the role of these external effects in explaining growth and economic convergence. We present a simple growth model, which includes externalities across economies, developing a methodology for testing their existence and estimating their strength. In our view, spatial econometrics is naturally suited to an empirical consideration of these externalities. We obtain evidence on the presence of significant externalities both across Spanish and European regions.

Public health expenditure and spatial interactions in a decentralized national health system

One of the limitations of cross-country health expenditure analysis refers to the fact that the financing, the internal organization and political restraints of health care decision-making are country-specific and heterogeneous. Yet, a potential solution is to examine the influence of such effects in those countries that have undertaken decentralization processes. In such a setting, it is possible to examine potential expenditure spillovers across the geography of a country as well as the influence of the political ideology of regional incumbents on public health expenditure. This paper examines the determinants of public health expenditure within Spanish region-states (Autonomous Communities, ACs), most of them subject to similar financing structures although exhibiting significant heterogeneity as a result of the increasing decentralization, region-specific political factors along with different use of health care inputs, economic dimension and spatial interactions

The empirics of spatial competition: evidence from European regions

The New Economic Geography literature allows detailed analysis of the factors that determine the location decisions of firms in integrated markets. However, the competitive process is modelled in a rather rudimentary way, and the empirical evidence has usually been obtained from reduced-form econometric specifications. This study describes a structural model that takes into account strategic interactions between firms. We investigate the relationship between the degree of perceived competition ¿ not only from local firms but from firms in other regions ¿ and geographic concentration. The preliminary results indicate that, in aggregate terms, local firms present stronger competition than firms in other regions. Moreover, it is confirmed that greater geographical concentration of production reduces market power, due to the intensification of local competition; however, its impact on production costs is unclear.

Spatial variability of aggregate stability in latosols under sugarcane

The spatial variability of soils under a same management system is differentiated, as expressed in the properties. The spatial variability of aggregate stability of a eutrophic Red Latosol (ERL) and a dystrophic Red Latosol (DRL) under sugarcane was characterized. Samples were collected in a regular 10 m grid, in the layers 0.0-0.2 and 0.2-0.4 m, with 100 points per area, and the following properties were determined: geometric mean diameter (GMD) of aggregates, mean weight diameter (MWD) of aggregates, percent of aggregates in the > 2.0 mm class and organic matter (OM) content. The eutrophic Red Latosol (ERL) had a higher aggregate stability thn the dystrophic Red Latosol (DRL), which may be attributed to the higher clay and OM content and the gibbsitic mineralogy of this soil class. The differentiated evolution of the studied Oxisols explains the wider range and lower variation coefficient and variability, for all properties studied in the eutrophic Red Latosol.