4,126 research outputs found

    Landslide and debris flow warning at regional scale. A real-time system using susceptibility mapping, radar rainfall and hydrometeorological thresholds

    Get PDF
    Rainfall triggered shallow slides and debris flows constitute a significant hazard that causes substantial economic losses and fatalities worldwide. Regional-scale risk mitigation for these processes is challenging. Therefore, landslide early warning systems (LEWS) are a helpful tool to depict the time and location of possible landslide events so that the hazardous situation can be managed more effectively. The main objective of this thesis is to set up a regional-scale LEWS that works in real-time over Catalonia (NE Spain). The developed warning system combines in real-time susceptibility information and rainfall observations to issue qualitative warnings over the region. Susceptibility has been derived combining slope angle and land use and land cover information with a simple fuzzy logic approach. The LEWS input rainfall information consists of high-resolution radar quantitative precipitation estimates (QPEs). To assess if a rainfall situation has the potential to trigger landslides, the LEWS applies a set of intensity duration thresholds. Finally, a warning matrix combines susceptibility and rainfall hazard to obtain a qualitative warning map that classifies the terrain into four warning classes. The evaluation of the LEWS performance has been challenging because of the lack of a systematic inventory, including the time and location of recent landslides events. Within the context of this thesis, a citizen-science initiative has been set up to gather landslide data from reports in social networks. However, some of the reports have significant spatial and temporal uncertainties. With the aim of finding the most suitable mapping unit for real-time warning purposes, the LEWS has been set-up to work using susceptibility maps based on grid-cells of different resolutions and subbasins. 30 m grid-cells have been chosen to compute the warnings as they offer a compromise between performance, interpretability of the results and computational costs. However, from an end users’ perspective visualising 30 m resolution warnings at a regional scale might be difficult. Therefore, subbasins have been proposed as a good option to summarise the warning outputs. A fuzzy verification method has been applied to evaluate the LEWS performance. Generally, the LEWS has been able to issue warnings in the areas where landslides were reported. The results of the fuzzy verification suggest that the LEWS effective resolution is around 1 km. The initial version of the LEWS has been improved by including soil moisture information in the characterisation of the rainfall situation. The outputs of this new approach have been compared with the outputs of LEWS using intensity-duration thresholds. With the new rainfall-soil moisture hydrometeorological thresholds, fewer false alarms were issued in high susceptibility areas where landslides had been observed. Therefore, hydrometeorological thresholds may be useful to improve the LEWS performance. This study provided a significant contribution to regional-scale landslide emergency management and risk mitigation in Catalonia. In addition, the modularity of the proposed LEWS makes it easy to apply in other regions.Els lliscaments superficials i els corrents d’arrossegalls són un fenomen perillós que causa significants perdudes econòmiques i humanes arreu del món. La seva principal causa desencadenant és la pluja. La mitigació del risc degut a aquets processos a escala regional no es senzilla. Ena quest context, els sistemes d’alerta són una eina útil per tal de predir el lloc i el moment en que es poden desencadenar possibles esllavissades en el futur, i poder fer una gestió del risc més eficient. L’objectiu principal d’aquesta tesi és el desenvolupament d’un sistema d’alerta per esllavissades a escala regional, que treballi en temps real a Catalunya. El Sistema d’alerta que s’ha desenvolupat combina informació sobre la susceptibilitat del terreny i estimacions de la pluja d’alta resolució per donar unes alertes qualitatives arreu del territori. La susceptibilitat s’ha obtingut a partir de la combinació d’informació del pendent del terreny, i els usos i les cobertes del sòl utilitzant un mètode de lògica difusa. Les dades de pluja són observacions del radar meteorològic. Per tal d’analitzar si un determinat episodi de pluja te el potencial per desencadenar esllavissades, el sistema d’alerta utilitza un joc de llindars intensitat-durada. Posteriorment, una matriu d’alertes combina la susceptibilitat i la magnitud del episodi de pluja. El resultat, és un mapa d’alertes que classifica el terreny en quatre nivells d’alerta. Amb l’objectiu de definir quina unitat del terreny és la més adient pel càlcul de les alertes en temps real, el sistema d’alerta s’ha configurat per treballar utilitzant mapes de susceptibilitat basats en píxels de diverses resolucions, i en subconques. Finalment, l’opció més convenient és utilitzar píxels de 30 m, ja que ofereixen un compromís entre el funcionament, la facilitat d’interpretació dels resultats i el cost computacional. Tot i això, la visualització de les alertes a escala regional emprant píxels de 30 m pot ser difícil. Per això s’ha proposat utilitzar subconques per oferir un sumari de les alertes. Degut a la manca d’un inventari d’esllavissades sistemàtic, que contingui informació sobre el lloc i el moment en que les esllavissades es van desencadenar, l’avaluació del funcionament del sistema d’alerta ha sigut un repte. En el context d’aquesta tesi, s’ha creat una iniciativa per tal de recol·lectar dades d’esllavissades a partir de posts en xarxes socials. Malauradament, algunes d’aquestes dades estan afectades per incerteses espacials i temporals força importants. Per a l’avaluació el funcionament del sistema d’alerta, s’ha aplicat un mètode de verificació difusa. Generalment, els sistema d’alerta ha estat capaç de generar alertes a les zones on s’havien reportat esllavissades. Els resultats de la verificació difusa suggereixen que la resolució efectiva del sistema d’alerta età al voltant d’1 km. Finalment, la versió inicial del sistema d’alerta s’ha millorat per tal poder incloure informació sobre l’estat d’humitat del terreny en la caracterització de la magnitud del episodi de pluja. Els resultats del sistema d’alerta utilitzant aquest nou enfoc s’han comparat amb els resultats que s’obtenen al córrer el sistema d’alerta utilitzant els llindars intensitat-durada. Mitjançant els nous llindars hidrometeorològics, el sistema emet menys falses alarmes als llocs on s’han desencadenat esllavissades. Per tant, utilitzar llindars hidrometeorològics podria ser útil per millorar el funcionament del sistema d’alerta dissenyat. L’estudi dut a terme en aquesta tesi suposa una important contribució que pot ajudar en la gestió de les emergències degudes a esllavissades a escala regional a Catalunya. A més a més, el fet de que el sistema sigui modular permet la seva fàcil aplicació en d’altres regions en un futur.Enginyeria del terren

    Air Quality Research Using Remote Sensing

    Get PDF
    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Data-assisted modeling of complex chemical and biological systems

    Get PDF
    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

    Get PDF
    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

    Get PDF
    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems

    Get PDF
    We propose and provide a proof of concept of a method to analyse, classify and compare dynamical systems of arbitrary dimensions by the two key features uncertainty and complexity. It starts by subdividing the system's time trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is then expressed by the mean entropy of all time slices. We define system complexity as “uncertainty about uncertainty” and express it by the entropy of the entropies of all time slices. Calculating and plotting uncertainty “u” and complexity “c” for many different numbers of time slices yields the c-u-curve. Systems can be analysed, compared and classified by the c-u-curve in terms of (i) its overall shape, (ii) mean and maximum uncertainty, (iii) mean and maximum complexity and (iv) characteristic timescale expressed by the width of the time slice for which maximum complexity occurs. We demonstrate the method with the example of both synthetic and real-world time series (constant, random noise, Lorenz attractor, precipitation and streamflow) and show that the shape and properties of the respective c-u-curve clearly reflect the particular characteristics of each time series. For the hydrological time series, we also show that the c-u-curve characteristics are in accordance with hydrological system understanding. We conclude that the c-u-curve method can be used to analyse, classify and compare dynamical systems. In particular, it can be used to classify hydrological systems into similar groups, a pre-condition for regionalization, and it can be used as a diagnostic measure and as an objective function in hydrological model calibration. Distinctive features of the method are (i) that it is based on unit-free probabilities, thus permitting application to any kind of data, (ii) that it is bounded, (iii) that it naturally expands from single-variate to multivariate systems, and (iv) that it is applicable to both deterministic and probabilistic value representations, permitting e.g. application to ensemble model predictions.</p

    Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions

    Get PDF
    This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality

    Adaptation pathways to reconcile hydropower generation and aquatic ecosystems restoration

    Get PDF
    The growing demands for water, food and energy, in addition to the need to protect ecosystems, pose significant challenges to water management and the operation of water systems. In hydropower-dominated basins, where reservoirs capture flow variability for energy generation, the modification of the natural flow regime disrupts the natural equilibrium of aquatic ecosystems. Migratory fish species and the associated ecosystem services are particularly vulnerable as the migration and recruitment success relies on the synchronization between the hydrologic flow regime and the reproductive cycle. While there is a consensus on the importance of restoring impacted ecosystems in balance with multiple uses, the current water governance framework lacks a comprehensive understanding of the tradeoffs involved and mechanisms for ensuring the equitable distribution of the adaptation costs among users. The present study brings a contribution to the field by proposing solutions to improve the water governance of river basins, combining the (1) identification of flow-ecological relationships by measuring the response of multiple options of flow regime restoration with a clear ecosystem indicator, (2) incorporation of the flow-ecological relationships and hydroclimatic conditions into the operation decisions of hydropower systems to create dynamic environmental flow solutions (termed Dynamic Adaptive Environmental flows – DAE-flows) with better long-term performance, (3) calculation of the reoperation trade-offs between alternative levels of environmental flow regime restoration and (4) development of mechanisms to share the adaptation costs among stakeholders. The electricity market is proposed as an institutional arrangement and financing mechanism to support the restoration of flow regimes in environmentally sensitive areas. The Upper Paraná River Basin, in Brazil, where consecutive hydropower impoundments have reduced the original floodplain along the last decades, is a recurrent example where reservoirs’ operation need to be reconciled with ecosystem functionality, which makes the basin an important study area. The findings of this dissertation indicate that it is possible to enhance the capacity of water systems to incorporate historically suppressed environmental water demands without imposing a hard constraint to economic uses. The consideration of the long-term effects of operation when designing operating strategies for multiple users leads to improved performance in both hydropower generation and meeting ecosystem demands. So, during severe droughts the water can still be reallocated to hydropower (as it is currently done) but at a lesser cost to the environment.As demandas crescentes por água, alimentos e energia, além da necessidade de proteger os ecossistemas, tornam a gestão dos recursos hídricos, bem como a operação de sistemas hídricos, uma tarefa desafiadora. Em bacias com aproveitamento hidrelétrico, a modificação do regime de vazões decorrente da operação dos reservatórios altera o equilíbrio natural dos ecossistemas aquáticos. Espécies migratórias de peixes e serviços ecossistêmicos associados ficam particularmente vulneráveis, uma vez que o sucesso da migração e recrutamento depende da sincronização entre o regime de vazão e o ciclo reprodutivo. Embora haja consenso sobre a importância de restaurar as demandas ecossistêmicas suprimidas e alcançar um equilíbrio que permita múltiplos usos, o atual quadro de governança carece de uma compreensão abrangente dos trade-offs envolvidos e dos mecanismos para garantir a distribuição equitativa dos custos de adaptação entre os usuários. O presente estudo contribui para o campo, propondo soluções para aprimorar a governança de bacias antropizadas, combinando (1) a identificação das relações vazão-ecológicas por meio da quantificação da resposta de múltiplas opções de restauração do regime de vazão por meio de um indicador de desempenho do ecossistema, (2) a incorporação dessas relações vazão-ecológicas juntamente com condições hidroclimáticas nas decisões operacionais de sistemas hidrelétricos (denominadas Vazões Ambientais Dinâmicas e Adaptativas - DAE-flows) para criar soluções dinâmicas de operação de reservatórios, (3) o cálculo dos trade-offs de reoperação de múltiplos níveis de restauração de regime de vazão ambiental e (4) o desenvolvimento de mecanismos para compartilhar os custos relacionados entre as partes interessadas. Nesse sentido, o mercado de eletricidade é proposto como arranjo institucional e mecanismo de financiamento para apoiar a restauração de regimes de vazão em áreas ambientalmente sensíveis. A Bacia Hidrográfica do Alto Paraná, Brasil, caracterizada como uma das mais represadas da América do Sul, com 65 usinas hidrelétricas integradas ao Sistema Integrado Nacional, é um exemplo recorrente da necessidade de reconciliação entre a geração de energia e a conservação de serviços ecossistêmicos, sendo utilizada como área de estudo. Os resultados indicam que podemos aumentar a capacidade dos sistemas hídricos para incorporar demandas ambientais historicamente suprimidas sem impor uma restrição rígida aos usos econômicos. Ao considerar os efeitos de longo prazo da operação ao projetar estratégias de operação para múltiplos usuários, obtemos um desempenho aprimorado tanto na geração de energia hidrelétrica quanto no atendimento às demandas do ecossistema. Assim, durante períodos de seca severa, a água ainda pode ser realocada para a produção de energia hidrelétrica (como é feito atualmente), porém com menor impacto ambiental
    corecore