Debris flow susceptibility mapping at a regional scale

Debris flow susceptibility mapping at a regional scale has been the subject of various studies. The complexity of the phenomenon and the variability of local controlling factors limit the use of process-based models for a first assessment. GISbased approaches associating an automatic detection of the source areas and a simple assessment of the debris flow spreading may provide a substantial basis for a preliminary susceptibility assessment at the regional scale. The use of a digital elevation model, with a 10 m resolution, for the Canton de Vaud territory (Switzerland), a lithological map and a land use map, has allowed automatic identification of the potential source areas. The spreading estimates are based on basic probabilistic and energy calculations that allow to define the maximal runout distance of a debris flow.

Maps as risk mitigation tools. Adaptation of the Swiss hazard assessment and mapping methodology to a Moroccan site: Beni Mellal

Dans le contexte climatique actuel, les régions méditerranéennes connaissent une intensification des phénomènes hydrométéorologiques extrêmes. Au Maroc, le risque lié aux inondations est devenu problématique, les communautés étant vulnérables aux événements extrêmes. En effet, le développement économique et urbain rapide et mal maîtrisé augmente l'exposition aux phénomènes extrêmes. La Direction du Développement et de la Coopération suisse (DDC) s'implique activement dans la réduction des risques naturels au Maroc. La cartographie des dangers et son intégration dans l'aménagement du territoire représentent une méthode efficace afin de réduire la vulnérabilité spatiale. Ainsi, la DDC a mandaté ce projet d'adaptation de la méthode suisse de cartographie des dangers à un cas d'étude marocain (la ville de Beni Mellal, région de Tadla-Azilal, Maroc). La méthode suisse a été adaptée aux contraintes spécifiques du terrain (environnement semi-aride, morphologie de piémont) et au contexte de transfert de connaissances (caractéristiques socio-économiques et pratiques). Une carte des phénomènes d'inondations a été produite. Elle contient les témoins morphologiques et les éléments anthropiques pertinents pour le développement et l'aggravation des inondations. La modélisation de la relation pluie-débit pour des événements de référence, et le routage des hydrogrammes de crue ainsi obtenus ont permis d'estimer quantitativement l'aléa inondation. Des données obtenues sur le terrain (estimations de débit, extension de crues connues) ont permis de vérifier les résultats des modèles. Des cartes d'intensité et de probabilité ont été obtenues. Enfin, une carte indicative du danger d'inondation a été produite sur la base de la matrice suisse du danger qui croise l'intensité et la probabilité d'occurrence d'un événement pour obtenir des degrés de danger assignables au territoire étudié. En vue de l'implémentation des cartes de danger dans les documents de l'aménagement du territoire, nous nous intéressons au fonctionnement actuel de la gestion institutionnelle du risque à Beni Mellal, en étudiant le degré d'intégration de la gestion et la manière dont les connaissances sur les risques influencent le processus de gestion. L'analyse montre que la gestion est marquée par une logique de gestion hiérarchique et la priorité des mesures de protection par rapport aux mesures passives d'aménagement du territoire. Les connaissances sur le risque restent sectorielles, souvent déconnectées. L'innovation dans le domaine de la gestion du risque résulte de collaborations horizontales entre les acteurs ou avec des sources de connaissances externes (par exemple les universités). Des recommandations méthodologiques et institutionnelles issues de cette étude ont été adressées aux gestionnaires en vue de l'implémentation des cartes de danger. Plus que des outils de réduction du risque, les cartes de danger aident à transmettre des connaissances vers le public et contribuent ainsi à établir une culture du risque. - Severe rainfall events are thought to be occurring more frequently in semi-arid areas. In Morocco, flood hazard has become an important topic, notably as rapid economic development and high urbanization rates have increased the exposure of people and assets in hazard-prone areas. The Swiss Agency for Development and Cooperation (SADC) is active in natural hazard mitigation in Morocco. As hazard mapping for urban planning is thought to be a sound tool for vulnerability reduction, the SADC has financed a project aimed at adapting the Swiss approach for hazard assessment and mapping to the case of Morocco. In a knowledge transfer context, the Swiss method was adapted to the semi-arid environment, the specific piedmont morphology and to socio-economic constraints particular to the study site. Following the Swiss guidelines, a hydro-geomorphological map was established, containing all geomorphic elements related to known past floods. Next, rainfall / runoff modeling for reference events and hydraulic routing of the obtained hydrographs were carried out in order to assess hazard quantitatively. Field-collected discharge estimations and flood extent for known floods were used to verify the model results. Flood hazard intensity and probability maps were obtained. Finally, an indicative danger map as defined within the Swiss hazard assessment terminology was calculated using the Swiss hazard matrix that convolves flood intensity with its recurrence probability in order to assign flood danger degrees to the concerned territory. Danger maps become effective, as risk mitigation tools, when implemented in urban planning. We focus on how local authorities are involved in the risk management process and how knowledge about risk impacts the management. An institutional vulnerability "map" was established based on individual interviews held with the main institutional actors in flood management. Results show that flood hazard management is defined by uneven actions and relationships, it is based on top-down decision-making patterns, and focus is maintained on active mitigation measures. The institutional actors embody sectorial, often disconnected risk knowledge pools, whose relationships are dictated by the institutional hierarchy. Results show that innovation in the risk management process emerges when actors collaborate despite the established hierarchy or when they open to outer knowledge pools (e.g. the academia). Several methodological and institutional recommendations were addressed to risk management stakeholders in view of potential map implementation to planning. Hazard assessment and mapping is essential to an integrated risk management approach: more than a mitigation tool, danger maps represent tools that allow communicating on hazards and establishing a risk culture.

Re-mapping the molecular features of the human immunodeficiency virus type 1 and human T-cell lymphotropic virus type 1 Brazilian sequences using a bioinformatics unit established in Salvador, Bahia, Brazil, to give support to the viral epidemiology studies

The analysis of genetic data for human immunodeficiency virus type 1 (HIV-1) and human T-cell lymphotropic virus type 1 (HTLV-1) is essential to improve treatment and public health strategies as well as to select strains for vaccine programs. However, the analysis of large quantities of genetic data requires collaborative efforts in bioinformatics, computer biology, molecular biology, evolution, and medical science. The objective of this study was to review and improve the molecular epidemiology of HIV-1 and HTLV-1 viruses isolated in Brazil using bioinformatic tools available in the Laboratório Avançado de Sáude Pública (Lasp) bioinformatics unit. The analysis of HIV-1 isolates confirmed a heterogeneous distribution of the viral genotypes circulating in the country. The Brazilian HIV-1 epidemic is characterized by the presence of multiple subtypes (B, F1, C) and B/F1 recombinant virus while, on the other hand, most of the HTLV-1 sequences were classified as Transcontinental subgroup of the Cosmopolitan subtype. Despite the high variation among HIV-1 subtypes, protein glycosylation and phosphorylation domains were conserved in the pol, gag, and env genes of the Brazilian HIV-1 strains suggesting constraints in the HIV-1 evolution process. As expected, the functional protein sites were highly conservative in the HTLV-1 env gene sequences. Furthermore, the presence of these functional sites in HIV-1 and HTLV-1 strains could help in the development of vaccines that pre-empt the viral escape process.

Debris flow susceptibility mapping at a regional scale along the National Road N7, Argentina

A first assessment of debris flow susceptibility at a large scale was performed along the National Road N7, Argentina. Numerous catchments are prone to debris flows and likely to endanger the road-users. A 1:50,000 susceptibility map was created. The use of a DEM (grid 30 m) associated to three complementary criteria (slope, contributing area, curvature) allowed the identification of potential source areas. The debris flow spreading was estimated using a process- and GISbased model (Flow-R) based on basic probabilistic and energy calculations. The best-fit values for the coefficient of friction and the mass-to-drag ratio of the PCM model were found to be ? = 0.02 and M/D = 180 and the resulting propagation on one of the calibration site was validated using the Coulomb friction model. The results are realistic and will be useful to determine which areas need to be prioritized for detailed studies.

Towards the Automatic Merging of Lexical Resources: Automatic Mapping

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to have richer resources with a broad range of potential uses for a significant number of languages.With the objective of reducing cost byeliminating human intervention, we present a new method for automating the merging of resources,with special emphasis in what we call the mapping step. This mapping step, which converts the resources into a common format that allows latter the merging, is usually performed with huge manual effort and thus makes the whole process very costly. Thus, we propose a method to perform this mapping fully automatically. To test our method, we have addressed the merging of two verb subcategorization frame lexica for Spanish, The resultsachieved, that almost replicate human work, demonstrate the feasibility of the approach.

In vivo fate mapping with SCL regulatory elements identifies progenitors for primitive and definitive hematopoiesis in mice.

One of the principal issues facing biomedical research is to elucidate developmental pathways and to establish the fate of stem and progenitor cells in vivo. Hematopoiesis, the process of blood cell formation, provides a powerful experimental system for investigating this process. Here, we employ transcriptional regulatory elements from the stem cell leukemia (SCL) gene to selectively label primitive and definitive hematopoiesis. We report that SCL-labelled cells arising in the mid to late streak embryo give rise to primitive red blood cells but fail to contribute to the vascular system of the developing embryo. Restricting SCL-marking to different stages of foetal development, we identify a second population of multilineage progenitors, proficient in contributing to adult erythroid, myeloid and lymphoid cells. The distinct lineage-restricted potential of SCL-labelled early progenitors demonstrates that primitive erythroid cell fate specification is initiated during mid gastrulation. Our data also suggest that the transition from a hemangioblastic precursors with endothelial and blood forming potential to a committed hematopoietic progenitor must have occurred prior to SCL-marking of definitive multilineage blood precursors.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Process Interfaces in an After Sales Organization

Työn tavoitteena oli selvittää Larox Oy:n, Lappeenranta, palveluorganisaation prosessit ja prosessien väliset rajapinnat. Prosesseja ja prosessien kehittämistä ja innovointia tarkasteltiin ensin kirjallisuuden perusteella. Rajapintojen selkeää esittämistä varten kehitettiin yksinkertainen metodologia mind mapping -tekniikan pohjalta. Nykyisten prosessien tila ja rajapinnat analysoitiin ja dokumentoitiin haastattelemalla Larox Oy:n työntekijöitä ja asiakkaita sekä tutustumalla prosessikuvauksiin ja muihin olennaisiin dokumentteihin. Analyysin tulosten perusteella tunnistettiin suurimmat ongelmakohdat rajapinnoissa ja pohdittiin mahdollisia ratkaisuja niihin. Pieniä prosessinkehitysaloitteita kehitettiin yhteistyössä Larox Oy:n työntekijöiden kanssa. Työn lopussa on pohdittu mahdollisia tulevaisuuden malleja Larox Oy:n palveluorganisaation toimintamalleiksi.

Electrostimulation mapping of comprehension of auditory and visual words.

In order to spare functional areas during the removal of brain tumours, electrical stimulation mapping was used in 90 patients (77 in the left hemisphere and 13 in the right; 2754 cortical sites tested). Language functions were studied with a special focus on comprehension of auditory and visual words and the semantic system. In addition to naming, patients were asked to perform pointing tasks from auditory and visual stimuli (using sets of 4 different images controlled for familiarity), and also auditory object (sound recognition) and Token test tasks. Ninety-two auditory comprehension interference sites were observed. We found that the process of auditory comprehension involved a few, fine-grained, sub-centimetre cortical territories. Early stages of speech comprehension seem to relate to two posterior regions in the left superior temporal gyrus. Downstream lexical-semantic speech processing and sound analysis involved 2 pathways, along the anterior part of the left superior temporal gyrus, and posteriorly around the supramarginal and middle temporal gyri. Electrostimulation experimentally dissociated perceptual consciousness attached to speech comprehension. The initial word discrimination process can be considered as an "automatic" stage, the attention feedback not being impaired by stimulation as would be the case at the lexical-semantic stage. Multimodal organization of the superior temporal gyrus was also detected since some neurones could be involved in comprehension of visual material and naming. These findings demonstrate a fine graded, sub-centimetre, cortical representation of speech comprehension processing mainly in the left superior temporal gyrus and are in line with those described in dual stream models of language comprehension processing.

Efficient image mosaicing for multi-robot visual underwater mapping

Robotic platforms have advanced greatly in terms of their remote sensing capabilities, including obtaining optical information using cameras. Alongside these advances, visual mapping has become a very active research area, which facilitates the mapping of areas inaccessible to humans. This requires the efficient processing of data to increase the final mosaic quality and computational efficiency. In this paper, we propose an efficient image mosaicing algorithm for large area visual mapping in underwater environments using multiple underwater robots. Our method identifies overlapping image pairs in the trajectories carried out by the different robots during the topology estimation process, being this a cornerstone for efficiently mapping large areas of the seafloor. We present comparative results based on challenging real underwater datasets, which simulated multi-robot mapping

Mapping financial stability

The ongoing global financial crisis has demonstrated the importance of a systemwide, or macroprudential, approach to safeguarding financial stability. An essential part of macroprudential oversight concerns the tasks of early identification and assessment of risks and vulnerabilities that eventually may lead to a systemic financial crisis. Thriving tools are crucial as they allow early policy actions to decrease or prevent further build-up of risks or to otherwise enhance the shock absorption capacity of the financial system. In the literature, three types of systemic risk can be identified: i ) build-up of widespread imbalances, ii ) exogenous aggregate shocks, and iii ) contagion. Accordingly, the systemic risks are matched by three categories of analytical methods for decision support: i ) early-warning, ii ) macro stress-testing, and iii ) contagion models. Stimulated by the prolonged global financial crisis, today's toolbox of analytical methods includes a wide range of innovative solutions to the two tasks of risk identification and risk assessment. Yet, the literature lacks a focus on the task of risk communication. This thesis discusses macroprudential oversight from the viewpoint of all three tasks: Within analytical tools for risk identification and risk assessment, the focus concerns a tight integration of means for risk communication. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. The overall task of this thesis is to represent high-dimensional data concerning financial entities on lowdimensional displays. The low-dimensional representations have two subtasks: i ) to function as a display for individual data concerning entities and their time series, and ii ) to use the display as a basis to which additional information can be linked. The final nuance of the task is, however, set by the needs of the domain, data and methods. The following ve questions comprise subsequent steps addressed in the process of this thesis: 1. What are the needs for macroprudential oversight? 2. What form do macroprudential data take? 3. Which data and dimension reduction methods hold most promise for the task? 4. How should the methods be extended and enhanced for the task? 5. How should the methods and their extensions be applied to the task? Based upon the Self-Organizing Map (SOM), this thesis not only creates the Self-Organizing Financial Stability Map (SOFSM), but also lays out a general framework for mapping the state of financial stability. This thesis also introduces three extensions to the standard SOM for enhancing the visualization and extraction of information: i ) fuzzifications, ii ) transition probabilities, and iii ) network analysis. Thus, the SOFSM functions as a display for risk identification, on top of which risk assessments can be illustrated. In addition, this thesis puts forward the Self-Organizing Time Map (SOTM) to provide means for visual dynamic clustering, which in the context of macroprudential oversight concerns the identification of cross-sectional changes in risks and vulnerabilities over time. Rather than automated analysis, the aim of visual means for identifying and assessing risks is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence, as well as external risk communication.

Mapping the Value Network Potential in Cross-sector Collaboration: Case welfare Services for Elderly in Finland

Value network has been studied greatly in the academic research, but a tool for value network mapping is missing. The objective of this study was to design a tool (process) for value network mapping in cross-sector collaboration. Furthermore, the study addressed a future perspective of collaboration, aiming to map the value network potential. During the study was investigated and pondered how to get the full potential of collaboration, by creating new value in collaboration process. These actions are parts of mapping process proposed in the study. The implementation and testing of the mapping process were realized through a case study of cross-sector collaboration in welfare services for elderly in the Eastern Finland. Key representatives in elderly care from public, private and third sectors were interviewed and a workshop with experts from every sector was also conducted in this regard. The value network mapping process designed in this study consists of specific steps that help managers and experts to understand how to get a complex value network map and how to enhance it. Furthermore, it make easier the understanding of how new value can be created in collaboration process. The map can be used in order to motivate participants to be engaged with responsibility in collaboration and to be fully committed in their interactions. It can be also used as a motivator tool for those organizations that intend to engage in collaboration process. Additionally, value network map is a starting point in many value network analyses. Furthermore, the enhanced value network map can be used as a performance measurement tool in cross-sector collaboration.