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This dissertation develops new approaches for hyperspectral image visualization. Double and multiple layers are proposed to effectively convey the abundant information contained in the original high-dimensional data for practical decision-making support. The contributions of this dissertation are as follows. 1.Development of new visualization algorithms for hyperspectral imagery. Double-layer technique can display mixed pixel composition and global material distribution simultaneously. The pie-chart layer, taking advantage of the properties of non-negativity and sum-to-one abundances from linear mixture analysis of hyperspectral pixels, can be fully integrated with the background layer. Such a synergy enhances the presentation at both macro and micro scales. 2.Design of an effective visual exploration tool. The developed visualization techniques are implemented in a visualization system, which can automatically preprocess and visualize hyperspectral imagery. The interactive tool with a userriendly interface will enable viewers to display an image with any desired level of details. 3.Design of effective user studies to validate and improve visualization methods. The double-layer technique is evaluated by well designed user studies. The traditional approaches, including gray-scale side-by-side classification maps, color hard classification maps, and color soft classification maps, are compared with the proposed double-layer technique. The results of the user studies indicate that the double-layer algorithm provides the best performance in displaying mixed pixel composition in several aspects and that it has the competitive capability of displaying the global material distribution. Based on these results, a multi-layer algorithm is proposed to improve global information display.

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Mathematical modeling of ocean waves is based on the formulation and solution of the appropriate equations of continuity, momentum and the choice of proper initial and boundary conditions. Under the influence of gravity, many free surface water waves can be modeled by the shallow water equations (SWE) with the assumption that the horizontal length scale of the wave is much greater than the depth scale and the wave height is much less than the fluid's mean depth. Furthermore, to describe three dimensional flows in the hydrostatic and Boussinesq limits, the multilayer SWE model is used, where the fluid is discretized horizontally into a set of vertical layers, each having its own height, density, horizontal velocity and geopotential. In this study, we used an explicit staggered finite volume method to solve single and multilayer SWE, with and without density stratification and bathymetry, to understand the dynamic of surface waves and internal waves. We implemented a two-dimensional version of the incompressible DYNAMICO method and compare it with a one-dimensional SWE. For multilayer SWE, we considered both two layer and a linear stratification of density, with very small density gradient, consistent with Boussinesq approximation. We used Lagrangian vertical coordinate which doesn't allow mass to flow across vertical layers. Numerical examples are presented to verify multilayer SWE model against single layer SWE, in terms of the phase speed and the steepness criteria of wave profile. In addition, the phase speed of the barotropic and baroclinic mode of two-layer SWE also verified our multilayer SWE model. We found that, for multilayer SWE, waves move slower than single layer SWE and get steeper than normal when they flow across bathymetry. A series of numerical experiment were carried out to compare 1-D shallow water solutions to 2-D solutions with and without density as well as to explain the dynamics of surface wave and internal wave. We found that, a positive fluctuations on free surface causes water to rise above surface level, gravity pulls it back and the forces that acquired during the falling movement causes the water to penetrate beneath it's equilibrium level, influences the generation of internal waves. Internal waves travel considerably more slowly than surface waves. On the other hand, a bumpy or a slicky formation of surface waves is associated with the propagation of internal waves. The interaction between these two waves is therefore demonstrated and discussed. / Thesis / Master of Science (MSc) / In the modelling of ocean wave, the formulation and solution of appropriate equations and proper initial and boundary conditions are required. The shallow water equations (SWE) are derived from the conservation of mass and momentum equations, in the case where the horizontal length scale of the wave is much greater than the depth scale and the wave height is much less than the fluid's mean depth. In multilayer SWE, the fluid is discretized horizontally into a set of vertical layers, each having its own height, density, horizontal velocity and geopotential. In this study, we used an explicit staggered finite volume method to solve single and multilayer SWE, with and without density stratification and bathymetry, to understand the dynamic of surface waves and internal waves. A series of numerical experiments were carried out to validate our multilayer model. It is found that, in the presence of density differences, surface waves for the multilayer SWE move slowly and get more steep than normal when they flow across bathymetry. Also, a positive fluctuations on free surface generates internal waves at the interior of ocean which propagate along the line of density gradient.

The IEEE 802.11 networks are vulnerable to numerous wireless-specific attacks. Attackers can implement MAC address spoofing techniques to launch these attacks, while masquerading themselves behind a false MAC address. The implementation of Intrusion Detection Systems has become fundamental in the development of security infrastructures for wireless networks. This thesis proposes the designing a novel security system that makes use of metrics from multiple layers of observation to produce a collective decision on whether an attack is taking place. The Dempster-Shafer Theory of Evidence is the data fusion technique used to combine the evidences from the different layers. A novel, unsupervised and self- adaptive Basic Probability Assignment (BPA) approach able to automatically adapt its beliefs assignment to the current characteristics of the wireless network is proposed. This BPA approach is composed of three different and independent statistical techniques, which are capable to identify the presence of attacks in real time. Despite the lightweight processing requirements, the proposed security system produces outstanding detection results, generating high intrusion detection accuracy and very low number of false alarms. A thorough description of the generated results, for all the considered datasets is presented in this thesis. The effectiveness of the proposed system is evaluated using different types of injection attacks. Regarding one of these attacks, to the best of the author knowledge, the security system presented in this thesis is the first one able to efficiently identify the Airpwn attack.

Situé au SE de l’Espagne, le Campo de Cartagena est un cas emblématique extrême des changements hydrologiques et environnementaux causés par l’utilisation intensive des eaux souterraines pour l’agriculture dans les zones semi-arides du pourtour méditerranéen. Le développement agricole a entraîné la surexploitation des horizons les plus profonds de l’aquifère multicouche, tandis que l’augmentation de la recharge liée au retour d’irrigation causait une remontée des niveaux dans la nappe superficielle libre. Un grand nombre de forages multi-crépinés permettent une connexion artificielle entre les différents aquifères, et donc un possible transfert de contaminants d’origine agricole depuis la nappe superficielle vers les nappes profondes. Suite à la montée des niveaux piézométriques dans la nappe superficielle, un débit permanent est maintenant observé dans le réseau hydrographique. S’y ajoutent les rejets incontrôlés de saumures issues d’usines privées de désalinisation d’eau souterraine, aboutissant eux aussi à l'exutoire du système, la lagune de la Mer Mineure. Dans ce contexte, comprendre la complexité de l’évolution du bilan hydrique est un enjeu scientifique majeur. Le premier axe de recherche a consisté en une large tâche de collecte et d’interprétation d’informations sur l’évolution du système aquifère multicouche au cours du siècle passé. Elle a permis de mettre en évidence l’inversion des gradients hydrauliques entre les trois premiers aquifères et des baisses de niveau piézométrique atteignant 500m. Malgré 40 ans de chroniques piézométriques et géochimiques, les possibilités d’interprétation étaient limitées par les rares descriptions techniques complètes de forages. Une méthode reposant sur la méthode d’apprentissage automatique Random Forest a donc été mise en œuvre et a permis d’identifier l’aquifère d’origine de 107 échantillons d’eau souterraine, avec des résultats bien meilleurs à ceux de l’Analyse Discriminante Linéaire (LDA) ou des arbres de décision (CART). Le deuxième axe de recherche a été motivé par la difficulté d’actualiser le bilan hydrique du système aquifère, où le retour d’irrigation s’ajoute à l’infiltration des précipitations comme source de recharge de la nappe superficielle. Des traceurs environnementaux (14C, 13C, 2H, 18O, 3H) ont été employés pour caractériser l’évolution à long terme de la recharge des nappes ainsi que les principaux processus de mélanges, discriminés entre échelle régionale (entre aquifères) et locale (au sein des forages). L’identification des échantillons correspondant à une recharge ancienne (pré-anthropisation) ou à une recharge moderne (post-anthropisation) à permis de quantifier une augmentation des taux de recharge dans la plaine de plus d’un ordre de grandeur en conséquence de la mise en place de l’activité agricole, soit de 10 mm.an-1 à 210 mm.an-1. Le troisième axe de recherche a été la quantification de la décharge sous-marine d’eau souterraine (SGD) vers la lagune de la Mer Mineure à l’aide des isotopes du radon (222Rn) et du radium (223Ra, 224Ra), combinés à une modélisation hydrodynamique de la lagune pour comprendre l’impact des apports anthropiques sur le bilan de radionucléides. Les principales zones de SGD ont été localisées, de même que la zone d’influence du panache de radionucléides issus des eaux de surface et des rejets de saumure. Finalement, les bilans de masse en été et en hiver aboutissent à des flux de SGD totaux (incluant les recirculation salines) de 7.2 à 15.9 108 m3.an-1 (222Rn), 21.9-44.7 108 m3.an-1 (224Ra) et 6.9 108 m3.an-1 (223Ra, hiver). Cette étude d'un cas extrême d’anthropisation présente un large intérêt bien au-delà du système aquifère multicouche du Campo de Cartagena ou de la région de Murcie. Les méthodes mises en place dans cette thèse pourront être appliquées dans d’autres sites méditerranéens où l’exploitation de l’eau souterraine suit une évolution comparable, continue et apparemment inexorable. / The Campo de Cartagena area in the Murcia region (SE Spain) is an emblematic case of the hydrological and environmental changes caused by the intensive use of groundwater for agriculture in semiarid Mediterranean areas. Agricultural development supported by the multi-layer aquifer system has led to the overexploitation of the deep layers, while irrigation return flow and the subsequent increased recharge rates increased water table levels in the unconfined layer. In addition, a large number of boreholes of the area are screened in several aquifers and allow an artificial connection between different groundwater masses. Moreover, as a consequence of the water table increase in the shallow aquifer, a permanent flow appeared in the last kilometres of the surface watershed. Together with the uncontrolled release of brines from private groundwater desalination, it induced a permanent surface flow of water to the main outlet of the system, the Mar Menor lagoon. In this context, understanding the complex evolution of the whole system and how the water balance is affected is a hard task.The first research focus aimed at collecting and reviewing all kind of existing data in order to reconstruct the evolution along one century of the multi-layer aquifer system. It highlighted an inversion of the vertical hydraulic gradient between aquifers and the decrease of water table levels up to 500 m for the deepest layer. Facing the difficult identification of the origin of groundwater samples, a method based on the Random Forest (RF) machine learning technique was developed. Despite the difficulty of an unequal dataset, accuracy over 90% was reached and 107 groundwater samples of unknown origin could be classified. Results were much better than using Linear Discriminant Analysis (LDA) or Decision Trees (CART).The second research axis was motivated by the difficulty to update the water balance of the multi-layer aquifer where irrigation return flow represents an additional source of recharge, added to the limited rainfall infiltration. Environmental tracers (14C, 13C, 2H, 18O, 3H) were combined to high-resolution temperature loggings to investigate the long-term evolution of recharge in the Campo de Cartagena aquifer system and discriminate local mixing processes (infra-boreholes) from regional mixing processes (between aquifers). Both pre-anthropization and post-anthropization recharge regime could be identified and quantified. Before the development of agriculture, recharge varied from 17 mm.a-1 in the mountain ranges to 6 mm.a-1 in the plain. In response to the increase of agricultural activity, recharge fluxes were amplified up to 210 mm.a-1 in irrigated areas.The third research axis consisted in quantifying submarine groundwater discharge (SGD). In order to decipher the influence of the different water sources on the Mar Menor, a radon (222Rn) and radium (223Ra, 224Ra) survey was combined with the hydrodynamic modeling of the lagoon. The areas of influence of a plume of radionuclides from the river were identified, the main areas of SGD were located and a location for a submarine emissary was proposed. Mass balances in winter and summer seasons provided total yearly SGD fluxes of water of 7.2-15.9 108 m3.a-1 (222Rn), 21.9-44.7 108 m3.a-1 (224Ra) and 6.9 108 m3.a-1 (223Ra, in winter). Water level effect, rather than tidal pumping, was identified as the main driver for recirculated saline groundwater, while fresh submarine groundwater discharge from the aquifer was about 1% of total SGD. As the case study is an extreme case of anthropization, these results present a wide interest that is not limited to the Murcia region or the Campo de Cartagena aquifer. The methods developed in this thesis might be used in other Mediterranean sites where groundwater exploitation seems to follow a continuous and inevitable increase.

Assessing the short-term socio-economic impacts of climate-led disasters on food trade networks requires new bottom-up models and vulnerability metrics rooted in complexity theory. Indeed, such shocks could generate cascading socio-economic losses across the networks layers where emerging agents¿ responses could trigger tipping points. We contribute to address this research gap by developing a multi-layer behavioral network methodology composed of multiple spatially-explicit layers populated by heterogeneous interacting agents. Then, by introducing a new multi-layer risk measure called vulnerability rank, or VRank, we quantify the stress in the aftermath of a shock. Our approach allows us to analyze both the supply- and the demand-side dimensions of the shock by quantifying short-term behavioral responses, the transmission channels across the layers, the conditions for reaching tipping points, and the feedback on macroeconomic indicators. By simulating a stylized two-layer supply-side production and demand-side household network model we find that, (i) socio-economic vulnerability to climate-led disasters is cyclical, (ii) the distribution of shocks depends critically on the network structure, and on the speed of supply-side and demand-side responses. Our results suggest that such a multi-layer framework could provide a comprehensive picture of how climate-led shocks cascade and how indirect losses can be measured. This is crucial to inform effective post-disaster policies aimed to build food trade network resilience to climate-led shocks, in particular in more agriculture-dependent bread-basket regions. / Series: Ecological Economic Papers

Identificação e predição do comportamento aeroelástico representa um grande desafio para a análise e controle de fenômenos aeroelásticos adversos. A modelagem aeroelástica requer informações tanto sobre a dinâmica estrutural quanto sobre o comportamento aerodinâmico não estacionário. No entanto, a maioria das metodologias disponíveis atualmente são baseadas no desacoplamento entre o modelo estrutural e o modelo aerodinâmico não estacionário. Conseqüentemente, métodos alternativos são bem vindos na área de pesquisa aerolástica. Entre os métodos alternativos está o funcional multicamada, que fornece uma rigorosa representação matemática apropriada para modelagem aeroelástica e pode ser obtido através de redes neurais artificiais. Esse trabalho apresenta uma aplicação desse método, consistindo de um procedimento de identificação baseado em redes neurais artificiais que representam o funcional da resposta aeroelástica. O modelo neural foi treinado usando o algoritmo de Levenberg-Marquardt, o qual tem sido considerado um método de otimização muito eficiente. Ele combina a garantia de convergência do método do gradiente e o alto desempenho do método de Newton, sem a necessidade de calcular as derivadas de segunda ordem. Um modelo de asa ensaiado em túnel de vento foi usado para fornecer a resposta aeroelástica. A asa foi fixada a uma mesa giratória e um motor elétrico lhe fornecia o movimento de incidência. Essa representação aeroelástica funcional foi testada para diversas condições operacionais do túnel de vento. Os resultados mostraram que o uso de redes neurais na identificação da resposta aeroelástica é um método alternativo promissor, o qual permite uma rápida avaliação da resposta aerolástica do modelo. / Identification and prediction of aeroelastic behavior presents a significant challenge for the analysis and control of adverse aeroelastic phenomena. Aeroelastic modeling requires information from both structural dynamics and unsteady aerodynamic behavior. However, the majority of methodologies available today are based on the decoupling of structural model from the unsteady aerodynamic model. Therefore, alternative methods are mostly welcome in the aeroelastic research field. Among the alternative methods there is the multi-layer functional (MLF), that allows a rigorous mathematical framework appropriate for aeroelastic modeling and can be realized by means of artificial neural networks. This work presents an identification procedure based on artificial neural networks to represent the motion-induced aeroelastic response functional. The neural network model has been trained using the Levenberg-Marquardt algorithm that has been considered a very efficient optimization method. It combines the guaranteed convergence of steepest descent and the higher performance of the Newton\'s method, without the necessity of second derivatives calculation. A wind tunnel aeroelastic wing model has been used to provide motion-induced aeroelastic responses. The wing has been fixed to a turntable, and an electrical motor provides the incidence motion to the wing. This aeroelastic functional representation is then tested for a range of the wind tunnel model operational boundaries. The results showed that the use of neural networks in the aeroelastic response identification is a promising alternative method, which allows fast evaluation of aeroelastic response model.

Orientador: Anna Diva Plasencia Lotufo / Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The present work aims to develop three models that are compared to determine if they can predict the firefighters' response load in a reasonable way. The tools chosen are the most representative from their respective categories in Machine Learning, such as XGBoost having as core a decision tree, a classic method such as Multi-Layer Perceptron and a more advanced algorithm like Long Short-Term Memory both with neurons as a base. The entire process is detailed, from data collection to obtaining the predictions. The results obtained prove a reasonable quality prediction that can be improved by data science techniques such as feature selection and adjustment of hyperparameters. / Resumo: Muitos fatores ambientais, econômicos e sociais estão levando as brigadas de incêndio a serem cada vez mais solicitadas e, como consequência, enfrentam um número cada vez maior de intervenções, na maioria das vezes com recursos constantes. Por outro lado, essas intervenções estão diretamente relacionadas à atividade humana, o que é previsível: os afogamentos em piscina ocorrem no verão, enquanto os acidentes de tráfego, devido a tempestades de gelo, ocorrem no inverno. Uma solução para melhorar a resposta dos bombeiros com recursos constantes é prever sua carga de trabalho, isto é, seu número de intervenções por hora, com base em variáveis explicativas que condicionam a atividade humana. O presente trabalho visa desenvolver três modelos que são comparados para determinar se eles podem prever a carga de respostas dos bombeiros de uma maneira razoável. As ferramentas escolhidas são as mais representativas de suas respectivas categorias em Machine Learning, como o XGBoost que tem como núcleo uma árvore de decisão, um método clássico como o Multi-Layer Perceptron e um algoritmo mais avançado como Long Short-Term Memory ambos com neurônios como base. Todo o processo é detalhado, desde a coleta de dados até a obtenção de previsões. Os resultados obtidos demonstram uma previsão de qualidade razoável que pode ser melhorada por técnicas de ciência de dados, como seleção de características e ajuste de hiperparâmetros. / Mestre

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