Esta tese apresenta uma análise numérica do controle de vibrações estruturais através de cerâmicas piezelétricas em extensão conectadas a circuitos ativo-passivos compostos por resistência, indutância e fonte de tensão. Para tal, um modelo de elementos finitos de vigas sanduíche com três camadas elásticas e/ou piezelétricas foi desenvolvido. Realizou-se também uma modelagem dos componentes do circuito elétrico e seu acoplamento à estrutura gerando assim uma equação de movimento acoplada para a estrutura com elementos piezelétricos conectados aos circuitos elétricos. Uma análise harmônica das equações obtidas foi realizada para se obter uma avaliação preliminar dos efeitos causados pelos componentes elétricos do circuito na estrutura. Observou-se que os elementos passivos do circuito, resistência e indutância, tem não somente um efeito de absorvedor dinâmico de vibrações mas, também, promovem uma amplificação da autoridade de controle no caso de se atuar através da fonte de tensão. Usando a metodologia tradicional de projeto de absorvedores dinâmicos de vibrações, derivou-se expressões para os valores de resistência e indutância de modo a maximizar o desempenho passivo do sistema. Uma análise do efeito de incertezas das constantes piezelétricas e dielétricas da cerâmica piezelétrica considerada e dos componentes de resistência e indutância do circuito elétrico no desempenho do controle passivo e ativo-passivo de estrutura tipo viga cantilever foi realizada. O objetivo desta análise foi quantificar robustez e sensibilidade do controle proposto. Em sequida, um estudo de otimização dos valores de resistência e indutância do circuito elétrico em função da tensão elétrica de controle máxima a ser aplicada em uma placa com diversos atuadores piezelétricos foi realizado. Finalmente e também para a estrutura tipo placa, uma análise de incertezas da rigidez da cola na interface entre estrutura e atuadores piezelétricos e seus efeitos no desempenho do controle passivo e ativo-passivo foi realizada. / This work presents a numerical analysis of the structural vibration control using piezoelectric materials in extension mode connected to active-passive electric circuits composed of resistance, inductance and voltage source. For that, a finite element model for sandwich beams with three elastic or piezoelectric layers was developed. A modeling of the electric circuit dynamics and its coupling to the structure with piezoelectric elements was also done. A harmonic analysis of the resulting equations was performed to yield a preliminary evaluation of the effects caused by the electric circuit components on the structure. It was observed that the passive circuit components not only lead to a dynamic vibration absorber effect but also to an amplification of the control authority in case of actuation using the voltage source. Using the standard methodology for the design of dynamic vibration absorbers, expressions were derived for the resistance and inductance values that optimize the passive vibration control performance of the system. An analysis of the effect of uncertainties of piezoelectric and dielectric constants of piezoelectric ceramic and resistance and inductance components of the shunt circuit on the passive and active-passive control performance for a cantilever beam structure was performed. The objective of this analysis was to quantify robustness and sensitivity of the proposed control. Then, an optimization study of the values of resistance and inductance of the shunt circuit as a function of the maximum control voltage to be applied on a plate with several piezoelectric actuators was performed. Finally and also for the plate structure, an analysis of uncertainties in the stiffness of the adhesive interface between structure and piezoelectric actuators and their effects on the performance of passive control and active-passive was performed.

A comprehensive assessment of water security incorporates a range of water-related concepts, since water policy issues to specific technical aspects of hydrological conditions and their interactions with societal needs and ecosystem functioning. This doctoral thesis is organized into three chapters that address such range of water security-related topics, aiming to establish a conceptual baseline and propose deterministic and stochastic accounting frameworks for a river basin water security evaluation. Specific assumptions and research questions are defined in each chapter, and are related to the management of \'Cantareira water supply system\' (located in Southeastern Brazil), focusing on different scales and on its political and hydrological aspects as well. The first chapter acts as a conceptual baseline for water security assessment, by examining general aspects of the Brazilian water policy and water allocation system. This study contrasts Brazilian and American water management systems applied to water transfer projects, discussing experiences from the \'Cantareira system\' and Colorado river basin. A deterministic accounting framework is presented in the second chapter, which is based on management of blue and green water kinds (defined in accordance with hydrological processes and storage types), and demonstrates how a quantitative analysis of provisioning and use (abstraction and consumption) of both water kinds can be conducted. An agricultural basin (291 km²) within the Cantareira water supply system (located upstream of the Cachoeira reservoir) was used to illustrate this approach. The impact of blue and green water use on median water resources conditions is accounted by the scarcity indicator, while the vulnerability indicator considers the probability of low availability of water resources. In the third chapter quantifies and discusses the impacts of uncertainties on water security indicators (proposed in the chapter 2), based on a multi-model and resampling framework, that considers several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) Environmental Flow Requirement methods; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. Then, the uncertainty is propagated through different methodological arrangements applied to the same study basin of chapter 2. In brief, the first chapter indicates that both Brazilian and American water management system can potentially contribute to each other. In the second chapter, the Blue/Green water-based accounting framework reveal clear spatial and temporal patterns of water scarcity and vulnerability levels within the basin, thereby improving our understanding of how and where water-related threats to human and aquatic ecosystem security can arise (so called hot-spots). The third chapter provide a general method that can form basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process. Further investigation are proposed in each research step of this doctoral thesis. / A avaliação da segurança hídrica pode incorporar vários conceitos relacionados à água, desde aspectos da política de recursos hídricos até questões hidrológicas específicas e suas interações com a sociedade e ecossistemas. Esta tese de doutorado busca estabelecer uma base conceitual e propor esquemas metodológicos com base determinística e estocástica para avaliação da segurança hídrica de bacias hidrográficas. Objetivos específicos são definidos em cada capítulo e relacionam-se à gestão do \'Sistema Cantareira de abastecimento de água\' (localizado no Sudeste do Brasil), com foco em diferentes escalas, bem como aspectos políticos e hidrológicos. O primeiro capítulo é apresentado como baseline conceitual, examinando aspectos gerais da política de recursos hídricos e sistemas alocação de água. Este estudo compara sistemas de gestão aplicados a projetos de transposição de água inter/intra-bacias no Brasil e Estados Unidos, discutindo experiências do Sistema Cantareira e da bacia do rio Colorado. O segundo capítulo, por sua vez, propõe e analisa um esquema metodológico determinístico baseado na gestão das águas azul e verde (definidas de acordo com processos hidrológicos e unidades de armazenamento). Este estudo demonstra como uma análise quantitativa da provisão e utilização de ambos os tipos de água pode ser conduzida, propondo indicadores de escassez e vulnerabilidade hídrica. Esta abordagem foi aplicada em uma bacia agrícola (291 km²), localizada a montante do reservatório Cachoeira, que é integrante do Sistema Cantareira. O terceiro capítulo quantifica e analisa os impactos das incertezas sobre os indicadores de segurança hídrica propostos no capítulo 2, utilizando um esquema metodológico estocástico baseado em múltiplos modelos e reamostragem, que incorpora variadas fontes de incerteza, tais como: i) dados observados de vazão; ii) estrutura do modelo hidrológico; iii) análise de resíduos do modelo hidrológico; iv) estimativa de vazão ambiental; v) definição de condições críticas de provisão e vi) demanda hídrica. Em seguida, as incertezas são propagadas através de diferentes arranjos metodológicos aplicados na mesma bacia estudo do capítulo 2. Em conclusão, o primeiro capítulo sugere uma potencial troca de contribuições provenientes de ambos os sistemas de gestão brasileiro e americano. O segundo capítulo revela padrões espaciais e temporais dos resultados dos indicadores de escassez e vulnerabilidade, melhorando assim a compreensão de como e onde ameaças à segurança hídrica podem surgir. Por sua vez, a análise de incertezas desenvolvida no terceiro capítulo é capaz de gerar suporte a gestores de recursos hídricos e processo de tomada de decisões robustas. Recomendações específicas são geradas em cada capítulo da presente tese de doutorado.

Le développement de la technologie des semi-conducteurs dans les trente dernières années a augmenté le nombre des nouvelles applications dans lesquelles les dispositifs d’électronique de puissance sont utilisés. L'augmentation de la rapidité de commutation des transistors a permis que la conversion de puissance se produise de façon de plus en plus performante. Cet avantage apporte un nouveau challenge dans la phase de conception, lié à la Compatibilité Électromagnétique. En effet, les impulsions rapides de tension et courant dans les convertisseurs de puissance sont une source d’émissions électromagnétiques conduites indésirables. Des méthodologies de modélisation précises, qui prennent en compte une grande partie des effets parasites, ont été développées pour évaluer le niveau de ces émissions conduites. Lorsque ces méthodologies sont confrontées aux mesures, les résultats sont en concordance dans une large gamme de fréquence, elles peuvent donc être considérées comme des outils fiables de pronostic. Néanmoins, la plupart des paramètres du modèle d’un système électronique ne peuvent pas réellement être déterminés précisément : les conditions d’opération sont souvent mal connues (variations de température ou d'humidité) ; les paramètres caractéristiques des composants présentent une certaine dispersion de production ; des interférences externes sont imprévisibles. Dans ce contexte, il est intéressant de développer des méthodologies de modélisation qui soient capables de prendre en compte des incertitudes paramétriques. Dans cette thèse, deux méthodologies d’analyse d’incertitudes, adaptées aux convertisseurs de puissance, sont proposées. Les incertitudes paramétriques sont modélisées en utilisant des fonctions de densité de probabilité et l’objectif de l’analyse proposée est de déterminer les moments statistiques, la fonction de densité de probabilité ou la limite supérieure probabiliste des émissions conduites d’un convertisseur de puissance quelconque. Des techniques pour aborder les difficultés liées aux non-linéarités, au temps de simulation important et au nombre élevé de dimensions sont discutées. Les méthodologies proposées sont appliquées à des problèmes test et à des problèmes réels, et les résultats sont comparés aux méthodologies classiques. La précision des résultats des méthodologies proposées est similaire aux techniques classiques, mais le temps de calcul est considérablement réduit. Finalement, ce travail ouvre des possibilités de développements nouveaux pour l’analyse des incertitudes des systèmes non-linéaires et à grande échelle. / The development of semiconductor technology in the last decades has boosted numerous new applications in which power electronic devices have been employed. The fast switching of transistors has allowed power conversion to be performed with high efficiency. However, this improvement brought a new challenge in design: the Electromagnetic Compatibility. Both fast pulses of voltage and current, inside power converters, are a source of unwanted conducted electromagnetic emissions. High accurate modeling methodologies, which takes into account most of the parasitic phenomena, have been developed, in order to compute the level of conducted emissions of electronic devices. When these methods are confronted with measurement, they show good agreement in a large frequency range, and thus they are considered a trustful prediction tool for electronic systems design. Nevertheless, most of the parameters of the model of any electronic system, in reality, cannot be determined precisely, due to unknown operation conditions (i.e.: temperature or humidity variations), production dispersion of the components or unpredictable external interference. In this context, it is of great interest to develop modeling methodologies that are able to take into account parametric uncertainties. In this thesis, two methodologies for uncertainty analysis of power converters are proposed. In the first, namely Polynomials per Frequency, the parametric uncertainty is modeled using probability density functions and the objective of the proposed analysis is to determine the statistical moments, the probability density function or a probabilistic upper bound for the conducted emissions of an arbitrary power converter. In the second methodology, namely Adaptive Unscented Transform, techniques to tackle the difficulties of nonlinearity, long simulation time and high-dimensionality are discussed. The proposed methodologies are applied to benchmark and real-world problems and the results are confronted to classical approaches. The accuracy of the gotten results is similar to those obtained by classical methods, although the required computational time is significantly reduced. Finally, this work leaves many possibilities for further development in the field of uncertainty analysis of nonlinear and highdimensional systems. / O desenvolvimento da tecnologia de semicondutores nas últimas décadas proporcionou um aumento no número de novas aplicações, nas quais dispositivos de eletrônica de potência são empregados. A rápida comutação dos transistores permitiu que a conversão de potência seja realizada com alta eficiência. Entretanto, esse benefício trouxe um novo desafio na fase de projeto: a Compatibilidade Eletromagnética. Os rápidos pulsos de tensão e corrente dentro dos conversores de potência são uma fonte indesejada de emissões eletromagnéticas conduzidas. Metodologias de modelagem de alta precisão, que consideram grande parte dos efeitos parasitas, foram desenvolvidas para avaliar o nível de emissões conduzidas de dispositivos eletrônicos. Estas metodologias, quando comparadas às medições, apresentam boa concordância numa ampla faixa de frequência, e portanto elas são consideradas ferramentas de previsão confiáveis para projeto de sistemas eletrônicos. Não obstante, a maioria dos parâmetros do modelo de um sistema eletrônico, na realidade, não podem ser determinados precisamente, devido às condições de operação incertas (por exemplo, variação de temperatura ou umidade), à dispersão de produção dos componentes ou à interferência externa imprevisível. Neste contexto, existe um grande interesse em desenvolver metodologias de modelagem que sejam capazes de levar em consideração incertezas paramétricas. Nesta tese, duas metodologias de análise de incertezas para conversores de potência são propostas. Na primeira, denominada Polinômios por Frequência, as incertezas paramétricas são modeladas usando funções densidade de probabilidade e o objetivo da análise proposta é determinar os momentos estatísticos, a função densidade de probabilidade ou o limite superior probabilístico das emissões conduzidas de um conversor de potência arbitrário. Na segunda, denominada Transformada de Incerteza Adaptativa, técnicas para abordar as dificuldades de nãolinearidade, tempo de simulação longo e alto número de dimensões são discutidas. As metodologias propostas são aplicadas em problemas teste e problemas do mundo real e os resultados são confrontados com metodologias clássicas. A precisão dos resultados das metodologias propostas é similar às técnicas clássicas, embora o tempo computacional necessário é significantemente reduzido. Finalmente, este trabalho deixa em aberto várias possibilidades para desenvolvimento adicional no campo da análise de incertezas de sistemas não-lineares e de alta-dimensão. / Развитие полупроводниковых технологий в последние десятилетия привело к росту числа новых приложений, в которых использовались силовые электронные устройства. Быстрое переключение транзисторов позволило силовой конверсии осуществляться с большей эффективностью. Однако это улучшение привело к новым сложностям в дизайне: Электромагнитная совместимость. Быстрое напряжение и токовые импульсы в силовых преобразователях являются источником нежелательного электромагнитного излучения. Высокоточные моделирующие методы, которые ведут учет большинства этих паразитарных явлений, были развиты для вычисления уровня управляемых излучений электронных устройств. Когда эти методы сопоставляются с измерениями, они показывают хорошее согласование в широком диапазоне частот, и, следовательно, они считаются надежным инструментом выявления для проектирования электронных систем. Тем не менее, большинство параметров модели любой электронной системы, в действительности, не могут быть точно определены при неизвестных условиях эксплуатации (т.е. температуры или влажности), производстве дисперсиикомпонентов или непредсказуемых внешних помехах. В этом контексте, это представляет большой интерес для разработки методов моделирования, которые способны учитывать параметрическую неопределенность. В этой диссертации предложены два метода анализа неопределенности силовых преобразователей. Параметрическая неопределенность моделируется с помощью функции плотности вероятности и цель предлагаемого анализа заключается в определении статистических моментов, функции плотности вероятности или вероятностной верхней границы кондуктивного излучения произвольного преобразователя питания. Техники по преодолению трудностей нелинейности, долгого времени симуляции и высокой размерности рассмотрены. Предлагаемые методики применяются для проверки и решения реальных проблем и результаты сравнимы с классическими подходами. Точность результатов похожа на классические методы, хотя время, требуемое для вычисления, существенно снижается. Наконец, эта работа оставляет много возможностей для дальнейшего развития в области неопределенности анализа нелинейных, многомерных систем.

L'instrumentation permet à notre environnement, maison ou bâtiment, de devenir intelligent en s'adaptant à nos modes de vie et en nous assistant au quotidien. Un environnement intelligent est sensible et réactif à nos activités, afin d'améliorer notre qualité de vie. La fiabilité d'identification des activités est ainsi essentielle pour cette intelligence ambiante : elle est directement dépendante du positionnement des capteurs au sein de l'environnement. Cette question essentielle du placement des capteurs est très peu considérée par les systèmes ambiants commercialisés ou même dans la littérature. Pourtant, elle est la source principale de leurs dysfonctionnements où une mauvaise reconnaissance des activités entraîne une mauvaise assistance fournie. Le placement de capteurs consiste à choisir et à positionner des capteurs pertinents pour une identification fiable des activités. Dans cette thèse, nous développons et détaillons une méthodologie de placement de capteurs axée sur l'identifiabilité des activités d'intérêt. Nous la qualifions en nous intéressant à deux évaluations différentes : la couverture des intérêts et l'incertitude de mesures. Dans un premier temps, nous proposons un modèle de l'activité où nous décomposons l'activité en actions caractérisées afin d'être indépendant de toute technologie ambiante (axée connaissances ou données). Nous représentons actions et capteurs par un modèle ensembliste unifiant, permettant de fusionner des informations homogènes de capteurs hétérogènes. Nous en évaluons l'identifiabilité des actions d'intérêt au regard des capteurs placés, par des notions de précision (performance d'identification) et de sensibilité (couverture des actions). Notre algorithme de placement des capteurs utilise la Pareto-optimalité pour proposer une large palette de placements-solutions pertinents et variés, pour ces multiples identifiabilités à maximiser. Nous illustrons notre méthodologie et notre évaluation en utilisant des capteurs de présence, et en choisissant optimalement la caractéristique à couvrir pour chaque action. Dans un deuxième temps, nous nous intéressons à la planification optimale des expériences où l'analyse de la matrice d'information permet de quantifier l'influence des sources d'incertitudes sur l'identification d'une caractéristique d'action. Nous représentons les capteurs continus et l'action caractérisée par un modèle analytique, et montrons que certaines incertitudes doivent être prises en compte et intégrées dans une nouvelle matrice d'information. Nous y appliquons les indices d'observabilité directement pour évaluer l'identifiabilité d'une action caractérisée (incertitude d'identification). Nous illustrons cette évaluation alternative en utilisant des capteurs d'angle, et nous la comparons à la matrice d'information classique. Nous discutons des deux évaluations abordées et de leur complémentarité pour la conception d’environnement instrumenté pour la veille à la personne. / Instrumentation enables our environment, house or building, to get smart through self-adjustment to our lifestyles and through assistance of our daily-life. A smart environment is sensitive and responsive to our activities, in order to improve our quality of life. Reliability of activities' identification is absolutely necessary to such ambient intelligence: it depends directly on sensors' positioning within the environment. This fundamental issue of sensor placement is hardly considered by marketed ambient systems or even into the literature. Yet, it is the main source of ambient systems' malfunctions and failures, because a bad activity recognition leads to a bad delivered assistance. Sensor placement is about choosing and positioning relevant sensors for a reliable identification of activities. In this thesis, we develop and detail a sensor placement methodology driven by identifiability of activities of interest. We quantify it by looking at two different evaluations: coverage of interests and uncertainty of measures. First, we present an activity model that decomposes each activity into characterised actions to be technology-free (either knowledge or data driven one). We depict actions and sensors by a set theoretic model, enabling to fuse homogeneous informations of heterogeneous sensors. We then evaluate each action of interest's identifiability regarding placed sensors, through notions of precision (identification's performance) and sensitivity (action's coverage). Our sensor placement algorithm use Pareto-optimality to offer a wide range of relevant solution-placements, for these multiple identifiabilities to maximise. We showcase our methodology and our evaluation through solving a problem featuring motion and binary sensors, by optimally choosing for each action the characteristic to cover. Finally, we look into optimal design of experiments by analysing the information matrix to quantify how sources of uncertainties influence the identification of an action's characteristic. We depict continuous sensors and the characterised action by an analytical model, and we show that some uncertainties should be considered and included in a new information matrix. We then apply directly observability indexes to evaluate identifiability of a characterised action (uncertainty of identification), and compare our new information matrix to the classical one. We showcase our alternate evaluation through solving a sensor placement problem featuring angular sensors. We discuss both covered evaluations and their complementarity towards the design of instrumented environment for human monitoring.

Simulationsmodelle sind ein etabliertes Instrument zur Analyse von Auswirkungen exogener Schocks in komplexen Systemen. Die in jüngster Zeit gestiegene verfügbare Rechenleistung und -geschwindigkeit hat die Entwicklung detaillierterer und komplexerer Simulationsmodelle befördert. Dieser Trend hat jedoch Bedenken hinsichtlich der Unsicherheit solcher Modellergebnisse aufgeworfen und daher viele Nutzer von Simulationsmodellen dazu motiviert, Unsicherheiten in ihren Simulationen zu integrieren. Eine Möglichkeit dies systematisch zu tun besteht darin, stochastische Elemente in die Modellgleichungen zu integrieren, wodurch das jeweilige Modell zu einem Problem (mehrfacher) numerischer Integrationen wird. Da es für solche Probleme meist keine analytischen Lösungen gibt, werden numerische Approximationsmethoden genutzt. Die derzeit zur Quantifizierung von Unsicherheiten in Simulationsmodellen genutzt en Techniken, sind entweder rechenaufwändig (Monte Carlo [MC] -basierte Methoden) oder liefern Ergebnisse von heterogener Qualität (Gauß-Quadraturen [GQs]). In Anbetracht der Bedeutung von effizienten Methoden zur Quantifizierung von Unsicherheit im Zeitalter von „big data“ ist es das Ziel dieser Doktorthesis, Methoden zu entwickeln, die die Näherungsfehler von GQs verringern und diese Methoden einer breiteren Forschungsgemeinschaft zugänglich machen. Zu diesem Zweck werden zwei neuartige Methoden zur Quantifizierung von Unsicherheiten entwickelt und in vier verschiedene, große partielle und allgemeine Gleichgewichtsmodelle integriert, die sich mit Agrarumweltfragen befassen. Diese Arbeit liefert methodische Entwicklungen und ist von hoher Relevanz für angewandte Simulationsmodellierer. Obwohl die Methoden in großen Simulationsmodellen für Agrarumweltfragen entwickelt und getestet werden, sind sie nicht durch Modelltyp oder Anwendungsgebiet beschränkt, sondern können ebenso in anderen Zusammenhängen angewandt werden. / Simulation models are an established tool for assessing the impacts of exogenous shocks in complex systems. Recent increases in available computational power and speed have led to simulation models with increased levels of detail and complexity. However, this trend has raised concerns regarding the uncertainty of such model results and therefore motivated many users of simulation models to consider uncertainty in their simulations. One way is to integrate stochastic elements into the model equations, thus turning the model into a problem of (multiple) numerical integration. As, in most cases, such problems do not have analytical solutions, numerical approximation methods are applied. The uncertainty quantification techniques currently used in simulation models are either computational expensive (Monte Carlo [MC]-based methods) or produce results of varying quality (Gaussian quadratures [GQs]). Considering the importance of efficient uncertainty quantification methods in the era of big data, this thesis aims to develop methods that decrease the approximation errors of GQs and make these methods accessible to the wider research community. For this purpose, two novel uncertainty quantification methods are developed and integrated into four different large-scale partial and general equilibrium models addressing agro-environmental issues. This thesis provides method developments and is of high relevance for applied simulation modelers who struggle to apply computationally burdensome stochastic modeling methods. Although the methods are developed and tested in large-scale simulation models addressing agricultural issues, they are not restricted to a model type or field of application.

[ES] La Evaluación Ambiental Estratégica (EAE) proporciona el marco para la articulación de los proyectos de forma coherente y respetuosa con el medio ambiente, condiciones sociales, políticas y económicas (Arce y Gullón, 2000) y aunque comúnmente se le conoce a la EAE como una herramienta, esta representa un proceso que puede mejorar la toma de decisiones y propiciar el desarrollo sostenible (Bidstrup y Hansen, 2014). El análisis de alternativas es una de las áreas más débiles de la EAE, debido a que no se ha investigado suficientemente qué funciona y qué no en el desarrollo, evaluación y selección de alternativas. Estas investigaciones deben incluir enfoques sistemáticos para la identificación y el desarrollo de alternativas, para involucrar plenamente a las partes interesadas en su definición y evaluación, y proporcionar detalles suficientes al documentar el proceso de la selección (González et al., 2018). A estas debilidades se añade la dificultad de desarrollar una metodología que sea adaptable a diferentes con-textos. Estos condicionantes han encauzado la construcción de la metodología desarrollada en esta tesis y así, lograr minimizar estas brechas, planteando un método analítico, simple y flexible. La metodología denominada "Índice de Selección de Alternativas Estratégicas Ambientales (ISAEA) y sus incertidumbres", parte de la utilización de indicadores simples construidos en base a las metas que se plantean en el instrumento evaluado en la EAE. Al construir los indicadores partiendo de los criterios de los propios objetivos o metas del Plan, Proyecto o Programa (PPP), se disminuye la complejidad y ambigüedad que típicamente existe en la construcción y/o selección de indicadores ambientales, pues queda claro la dirección que debe seguirse en la construcción del indicador. Los criterios de construcción permiten optimizar la calidad del indicador, ya que el mismo describe el objetivo y, por ende, se manejan indicadores que son los adecuados para ese PPP en particular, es decir, adaptado al contexto preciso del PPP. El ISAEA se complementa con un análisis de incertidumbre, que ofrece al tomador de decisiones herramientas completas para proceder a seleccionar con información cuantitativa, la alternativa que más conveniente para el PPP estudiado. De este modo, la metodología propuesta proporciona un índice que representa en qué medida se acerca la alternativa a la meta planteada, aportando además una adaptación de los análisis de incertidumbre al uso de indicadores compuestos. La experiencia de la aplicación de esta metodología en ambos de sus componentes principales, el ISAEA y el análisis de incertidumbre, deja de manifiesto la importancia de la cooperación abierta de la entidad promotora del PPP para garantizar el flujo de información y más importante aún, de información confiable. Tras el desarrollo de la presente investigación se concluye que la metodología desarrollada aporta una herramienta valiosa, que puede ser utilizada de forma simple y a la vez permite reflejar las circunstancias propias del PPP. A la vez, la metodología ofrece resultados en un lenguaje matemático llano, fácilmente entendible para los tomadores de decisiones. La metodología que se aporta en esta investigación doctoral llega a minimizar una brecha en la temática de selección de alternativas en la EAE, ya que se basa en información cuantitativa y a la vez ha asumido un desafío pendiente en evaluaciones ambientales al incorporar el análisis de incertidumbre como un criterio de acompañamiento al ISAEA, que permite realizar una selección de alternativas estratégicas en base a criterios científicos. / [CA] L'Avaluació Ambiental Estratègica (AAE) proporciona el marc per a l'articulació dels projectes de forma coherent i respectuosa amb el medi ambient, condicions socials, polítiques i econòmiques (Arce i Gullón, 2000) i encara que comunament se li coneix a la AAE com una eina, aquesta representa un procés que pot millorar la presa de decisions i propiciar el desenvolupament sostenible (Bidstrup i Hansen, 2014). L'anàlisi d'alternatives és una de les àrees més febles de la AAE, pel fet que no s'ha investigat prou què funciona i què no en el desenvolupament, avaluació i selecció d'alternatives. Aquestes investigacions han d'incloure enfocaments sistemàtics per a la identificació i el desenvolupament d'alternatives, per involucrar plenament a les parts interessades en la seva definició i avaluació, i proporcionar detalls suficients al documentar el procés de la selecció (González et al., 2018). A aquestes debilitats s'afegeix la dificultat de desenvolupar una metodologia que sigui adaptable a diferents contextos. Aquests condicionants han canalitzat la construcció de la metodologia desenvolupada en aquesta tesi i així, aconseguir minimitzar aquestes bretxes, plantejant un mètode analític, simple i flexible. La metodologia anomenada "Índex de Selecció d'Alternatives Estratègiques Ambientals (ISAEA) i les seves incerteses", part de la utilització de indicadors simples construïts en base a les metes que es plantegen en el instrument avaluat en l'AAE. Al construir els indicadors partint dels criteris dels propis objectius o metes de el Pla, Projecte o Programa (PPP), es disminueix la complexitat i ambigüitat que típicament existeix en la construcció i/o selecció d'indicadors ambientals, ja que queda clar la direcció que ha de seguirse en la construcció de l'indicador. Els criteris de construcció permeten optimitzar la qualitat de l'indicador, ja que el mateix descriu el objectiu i, per tant, es manegen indicadors que són els adequats per a aquest PPP en particular, és a dir, adaptat al context precís de l'PPP. L'ISAEA es complementa amb una anàlisi d'incertesa, que ofereix al prenedor de decisions eines completes per a procedir a seleccionar amb informació quantitativa, l'alternativa que més convenient per al PPP estudiat. D'aquesta manera, la metodologia proposada proporciona un índex que representa en quina mesura s'acosta l'alternativa a la meta plantejada, aportant a més una adaptació de les anàlisis d'incertesa a l'ús d'indicadors com-llocs. L'experiència de l'aplicació d'aquesta metodologia en tots dos dels seus components principals, l'ISAEA i l'anàlisi d'incertesa, deixa de manifest la importància de la cooperació oberta de l'entitat promotora de l'PPP per garantir el flux d'informació i, més important encara, d'informació ambfiable. Després de concloure la present investigació queda demostrat que la metodologia desenvolupada aporta una eina valuosa, que pot ser utilitzada de forma simple i alhora permet reflectir les circumstàncies pròpies de l'PPP. Alhora, la metodologia ofereix resultats en un llenguatge matemàtic pla, fàcilment comprensible per als prenedors de decisions. La metodologia que s'aporta en aquesta tesi doctoral arriba a minimitzar una bretxa en la temàtica de selecció d'alternatives a l'EAE, ja que es basa en informació quantitativa i alhora ha assumit un repte pendent en avaluacions ambientals a l'incorporar el anàlisi d'incertesa com un criteri d'acompanyament a l'ISAEA, que permet realitzar una selecció d'alternatives estratègiques en base a criteris científics / [EN] Strategic Environmental Assessment (SEA) provides the framework for the articulation of projects in a coherent and respectful way with the environment, social, political and economic conditions (Arce and Gullón, 2000). Although SEA is commonly known as a tool, it represents a process that can improve decision-making and promote sustainable development (Bidstrup and Hansen, 2014). The analysis of alternatives is one of the weakest areas of SEA, because what works and what does not in the development, evaluation and selection of alternatives has not been sufficiently investigated. These investigations should include systematic ap-proaches for the identification and development of alternatives, to fully involve stake-holders in their definition and evaluation, and provide sufficient detail when documenting the selection process (González et al., 2018). Besides these weaknesses, the difficulty of developing a methodology that is adaptable to different contexts appears. These conditioning factors have guided the construction of the methodology developed in this thesis and thus, manage to minimize these gaps, proposing an analytical, simple and flexible method. The methodology called "Selection Index for Strategic Environmental Alternatives (ISAEA) and its uncertainties", starts from the use of simple indicators built on the basis of the goals set out in the instrument evaluated by the SEA. By constructing the indicators based on the criteria of the objectives or goals of the Plan, Project or Program (PPP), the complexity and ambiguity that typically exists in the construction and/or selection of environmental indicators is reduced, since the direction to be followed in the construction of the indicator. The construction criteria allow optimizing the quality of the indicator, since it describes the objective and, therefore, indicators are used that are appropriate for that particular PPP, that is, adapted to the precise context of each PPP. The ISAEA is complemented with an uncertainty analysis, which offers the decision maker complete tools to proceed to select with quantitative information, the most convenient alternative for the PPP studied. Therefore, the proposed methodology provides an index that represents to what extent the alternative approaches the proposed goal, also providing an adaptation of the uncertainty analyzes to the use of composite indicators. The experience of applying this methodology in both of its main components, the ISAEA and the uncertainty analysis, shows the importance of the open cooperation of the PPP promoter to guarantee the flow of information and, more importantly, reliable information. After the development of this research, it is concluded that the developed methodology provides a valuable tool, which can be used in a simple way while allowing reflecting the circumstances of the PPP. At the same time, the methodology offers results in plain mathematical language, easily understood by decision makers. The methodology provided in this doctoral research minimizes a gap in the subject of selecting alternatives in SEA, since it is based on quantitative information and at the same time has assumed a pending challenge in environmental evaluations by incorporating the uncertainty analysis as an accompanying criterion to the ISAEA, which allows a selection of strategic alternatives based on scientific criteria. / González González, RL. (2022). Metodología analítica para el análisis de alternativas en la evaluación ambiental estratégica y sus incertidumbres en la toma de decisiones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/183152 / TESIS

Hydraulisk modellering är ett viktigt verktyg vid utvärdering av lämpliga åtgärder för torrfåror. Modelleringen påverkas dock alltid av osäkerheter och om dessa är stora kan en modells simuleringsresultat bli opålitligt. Det kan därför vara viktigt att presentera dess simuleringsresultat tillsammans med osäkerheter. Denna studie utreder olika typer av osäkerheter som kan påverka hydrauliska modellers simuleringsresultat. Dessutom utförs känslighetsanalyser där en andel av osäkerheten i simuleringsresultatet tillskrivs de olika inmatningsvariablerna som beaktas. De parametrar som ingår i analysen är upplösningen i använd terrängmodell, upplösning i den hydrauliska modellens beräkningsnät, inflöde till modellen och råheten genom Mannings tal. Studieobjektet som behandlades i denna studie var en torrfåra som ligger nedströms Sandforsdammen i Skellefteälven och programvaran TELEMAC-MASCARET nyttjades för samtliga hydrauliska simuleringar i denna studie.  För att analysera osäkerheter kopplade till upplösning i en terrängmodell och ett beräkningsnät användes ett kvalitativt tillvägagångsätt. Ett antal simuleringar utfördes där alla parametrar förutom de kopplade till upplösning fixerades. Simuleringsresultaten illustrerades sedan genom profil, sektioner, enskilda raster och raster som visade differensen mellan olika simuleringar. Resultaten för analysen visade att en låg upplösning i terrängmodeller och beräkningsnät kan medföra osäkerheter lokalt där det är högre vattenhastigheter och där det finns stor variation i geometrin. Några signifikanta effekter kunde dock inte skönjas på större skala.  Separat gjordes kvantitativa osäkerhets- och känslighetsanalyser för vattendjup och vattenhastighet i torrfåran. Inmatningsparametrarna inflöde till modellen och råhet genom Mannings tal ansågs medföra störst påverkan och övriga parametrar fixerades således. Genom script skapade i programmeringsspråket Python tillsammans med biblioteket OpenTURNS upprättades ett stort urval av möjliga kombinationer för storlek på inflöde och Mannings tal. Alla kombinationer som skapades antogs till fullo täcka upp för den totala osäkerheten i inmatningsparametrarna. Genom att använda urvalet för simulering kunde osäkerheten i simuleringsresultaten också beskrivas. Osäkerhetsanalyser utfördes både genom klassisk beräkning av statistiska moment och genom Polynomial Chaos Expansion. En känslighetsanalys följde sedan där Polynomial Chaos Expansion användes för att beräkna Sobols känslighetsindex för inflödet och Mannings tal i varje kontrollpunkt. Den kvantitativa osäkerhetsanalysen visade att det fanns relativt stora osäkerheter för både vattendjupet och vattenhastighet vid behandlat studieobjekt. Flödet bidrog med störst påverkan på osäkerheten medan Mannings tals påverkan var insignifikant i jämförelse, bortsett från ett område i modellen där dess påverkan ökade markant. / Hydraulic modelling is an important tool when measures for dry river stretches are assessed. The modelling is however always affected by uncertainties and if these are big the simulation results from the models could become unreliable. It may therefore be important to present its simulation results together with the uncertainties. This study addresses various types of uncertainties that may affect the simulation results from hydraulic models. In addition, sensitivity analysis is conducted where a proportion of the uncertainty in the simulation result is attributed to the various input variables that are included. The parameters included in the analysis are terrain model resolution, hydraulic model mesh resolution, inflow to the model and Manning’s roughness coefficient. The object studied in this paper was a dry river stretch located downstream of Sandforsdammen in the river of Skellefteälven, Sweden. The software TELEMAC-MASCARET was used to perform all hydraulic simulations for this thesis.  To analyze the uncertainties related to the resolution for the terrain model and the mesh a qualitative approach was used. Several simulations were run where all parameters except those linked to the resolution were fixed. The simulation results were illustrated through individual rasters, profiles, sections and rasters that showed the differences between different simulations. The results of the analysis showed that a low resolution for terrain models and meshes can lead to uncertainties locally where there are higher water velocities and where there are big variations in the geometry. However, no significant effects could be discerned on a larger scale.  Separately, quantitative uncertainty and sensitivity analyzes were performed for the simulation results, water depth and water velocity for the dry river stretch. The input parameters that were assumed to have the biggest impact were the inflow to the model and Manning's roughness coefficient. Other model input parameters were fixed. Through scripts created in the programming language Python together with the library OpenTURNS, a large sample of possible combinations for the size of inflow and Manning's roughness coefficient was created. All combinations were assumed to fully cover the uncertainty of the input parameters. After using the sample for simulation, the uncertainty of the simulation results could also be described. Uncertainty analyses were conducted through both classical calculation of statistical moments and through Polynomial Chaos Expansion. A sensitivity analysis was then conducted through Polynomial Chaos Expansion where Sobol's sensitivity indices were calculated for the inflow and Manning's M at each control point. The analysis showed that there were relatively large uncertainties for both the water depth and the water velocity. The inflow had the greatest impact on the uncertainties while Manning's M was insignificant in comparison, apart from one area in the model where its impact increased.

The aim of this work is the improvement of model-based data worth analysis for groundwater systems with the use of surrogate models. Physically-based groundwater models are wide-spread tools used to make diverse predictions for research and management problems. They allow incorporation of system knowledge and a multitude of data. Often, this complexity is accompanied with high model run times. This is especially problematic for applications such as uncertainty analysis or data worth analysis, which necessitate many model runs. Surrogate models aim to address these challenges with run time reduction through simplification of the model, usually through techniques of the following three major categories: projection-based methods, data-driven methods and structural reduction methods. The run time reduction through the use of surrogate models is associated with impairments regarding their applicability, accuracy of system representation, predictive uncertainty quantification and integration of system knowledge. In light of these potential limitations, this thesis compares the ability of three different surrogate models in reproducing diverse model predictions and data worth estimates of a complex, real-world benchmark model. The surrogates used are a spatially and parametrically simplified physically-based model, a set of artificial neural networks (ANNs) and a projection-based 'proper orthogonal decomposition' (POD) model. In the first part of this dissertation, the potentials and shortcomings of the popular POD method in regard to boundary representation are detailed and an extension of the method for accurate boundary depiction is proposed. The explicit treatment of boundary conditions is shown to eliminate reduction-induced errors at Dirichlet and Neumann boundaries (and reduce errors at Cauchy boundaries) for a small trade-off in general groundwater head accuracy. Ease of implementation and the potential for purposeful application of the extension allow modelers a target-oriented refinement of POD models. The second part of this dissertation addresses the challenge of quantifying the model simplification error of a surrogate model in light of erroneousness of their model predictions. An existing method for predictive uncertainty quantification is extended to estimate the simplification error and bias of different model predictions of all three surrogate models compared to the complex benchmark model predictions. Results show that the magnitude and structure of model simplification error is highly dependent on both the type of model prediction and surrogate model. In the final part of the thesis, two of the surrogate models are compared with the complex model in their application for analysis of worth of different data types. First-order second-moment data worth analysis methods are extended to account for the non-uniqueness of calibrated model parameters in a robust method. It is then used in collaboration with the surrogate models to analyze the worth of existing, 'future' and 'parametric' data for varying model predictions. The comparison of changes in predictive uncertainty variance between complex model and the surrogates shows that the simplified, physically-based model is only able to identify data worth in the existing calibration data set. The POD model is a suitable surrogate for data worth analysis in regards to all differing predictions and worth of existing, 'future' and 'parametric' data when combined with the strengths of the proposed robust data worth analysis method.:1 Introduction 2 State of the art 2.1 Surrogate modeling for groundwater systems 2.1.1 Introduction 2.1.2 Projection-based methods 2.1.3 Data-driven methods 2.1.4 Structural simplification methods 2.1.5 Open research questions 2.2 Uncertainty and data worth analysis 2.2.1 Introduction 2.2.2 Sources of uncertainty in groundwater modeling 2.2.3 Types of uncertainty analysis 2.2.4 Data worth analysis 2.2.5 Open research questions 3 Objectives and contributions 3.1 Explicit boundary treatment in POD 3.2 Analysis of model simplification error 3.3 Robust data worth analysis using surrogate models 3.4 Expected impact 4 Methods 4.1 The Wairau Plain aquifer, the complex model and its surrogates 4.1.1 The Wairau Plain aquifer 4.1.2 Complex MODFLOW model of the Wairau Plain aquifer (CM) 4.1.3 Surrogate 1: simplified MODFLOW model (SM1,sMm) 4.1.4 Surrogate 2: linearized POD model (SM2,POD) 4.1.5 Surrogate 3: artificial neural networks (SM3,ANN) 4.2 POD extension for explicit boundary treatment 4.2.1 Groundwater models and basic POD 4.2.2 Theory of explicit treatment of boundary conditions in POD 4.2.3 Different boundary conditions in eb-POD 4.2.4 Cost of eb-POD compared to basic POD 4.3 Model simplification error analysis - theory 4.3.1 A linear model, solution space and null-space 4.3.2 Surrogate model: definition and calibration 4.3.3 Parameter simplification - relationship between complex model and surrogate model parameters 4.3.4 Simplification error of surrogate model predictions 4.4 Model simplification error analysis - scatter plot analysis 4.4.1 Methodology 4.4.2 General features of the scatter plots 4.4.3 Contributions of error terms 4.4.4 Prediction pairs 4.4.5 Summary 4.5 Robust data worth analysis 4.5.1 First-order second-moment uncertainty estimation 4.5.2 Worth of data 4.5.3 Generating calibrated parameter sets - null-space parameter perturbation 4.5.4 Robust data worth analysis 5 Results and discussion 5.1 Explicit treatment of boundary conditions in POD 5.1.1 (Variable) Dirichlet boundaries 5.1.2 Neumann boundaries 5.1.3 Cauchy boundaries 5.1.4 Applying eb-POD: summary 5.2 Quantifying model simplification error 5.2.1 Simplified MODFLOW model: SM1,sMm 5.2.2 POD surrogate model: SM2,POD 5.2.3 ANN surrogate model: SM3,ANN 5.2.4 Surrogate comparison: simplification errors in model predictions 5.3 Robust data worth analysis using surrogate models 5.3.1 Worth of existing data 5.3.2 Worth of 'future' data 5.3.3 Worth of 'parametric' data 5.3.4 Data worth with surrogate models: summary 5.4 Discussion 6 Conclusions and outlook A Appendix: Publications / Das Ziel dieser Arbeit ist die Verbesserung modellbasierter Datenwertanalyse in Grundwassersystemen mittels Ersatzmodellen. Vielfältige Fragestellungen im Fachgebiet Grundwasser werden in Wissenschaft und Praxis mithilfe von physikalisch basierten Modellen beantwortet. Solche Modelle können hierbei vorhandene Kenntnisse über das System sowie eine Vielzahl gemessener Daten einbinden. Jedoch führt die zugrundeliegende Komplexität häufig zu langen Modelllaufzeiten, was insbesondere für Anwendungen mit vielen Modellläufen, wie Unsicherheits- oder Datenwertanalyse, problematisch ist. Hier greifen Ersatzmodelle an: eine Vereinfachung des Modells reduziert die zugehörigen Laufzeiten. Solche Ersatzmodelle lassen sich in drei Kategorien einteilen: projektionsbasierte Methoden, datengetriebene Methoden und strukturreduzierende Methoden. Ersatzmodelle haben jedoch auch verschiedene Nachteile: eingeschränkte Anwendbarkeit, geringere Genauigkeit der Systemabbildung, verringerte Qualität ihrer Vorhersage- und Unsicherheitsschätzung, sowie Restriktionen bei der Integrierung vorhandener Systemkenntnisse. Zur Abschätzung dieser möglichen Einschränkungen vergleicht diese Arbeit drei verschiedene Ersatzmodelle mit einem realen, komplexen Benchmarkmodell in Bezug auf ihre Fähigkeit, verschiedene Modellvorhersagen und Datenwertabschätzungen glaubhaft zu reproduzieren. Die drei Ersatzmodelle sind: ein räumlich und parametrisch vereinfachtes, physikalisch basiertes Modell, ein Verbund künstlicher neuronaler Netze und ein projektionsbasiertes 'proper orthogonal decomposition' (POD) Modell. Der erste Teil der Arbeit widmet sich den Ungenauigkeiten der weitverbreiteten POD Methode bei der Abbildung von Randbedingungen und zeigt eine Erweiterung der Methode zur präziseren Darstellung der Randbedingungen in POD auf. Es wird nachgewiesen, dass diese explizite Behandlung der Randbedingungen in POD reduktionsbasierte Fehler in den Dirichlet- und Neumann-Randbedingungen eliminiert sowie Fehler in den Cauchy-Randbedingungen reduziert. Die Methode führt jedoch zu einer leicht verringerten Genauigkeit bei der allgemeinen Abbildung der Grundwasserstände im Vergleich zu herkömmlichen POD. Dennoch ist die Methode ein nützliches Werkzeug, da sie eine zielgerichtete Anpassung von POD-Ersatzmodellen erlaubt. Der zweite Teil der Arbeit stellt sich der Aufgabe, den durch die Modellvereinfachung eingebrachten Fehler der Ersatzmodelle bezogen auf ihre Modellvorhersagen zu beurteilen. Hierzu wird eine vorhandene Methode zur Berechnung von Vorhersageunsicherheiten angepasst und erweitert, um Fehler und Bias durch Modellvereinfachung zwischen den drei Ersatzmodellen und dem Benchmarkmodell für verschiedene Modellvorhersagen abzuschätzen. Die Ergebnisse zeigen, dass Größe und Struktur des Fehlers durch Modellvereinfachung stark von der Art der Modellvorhersage und des jeweiligen Ersatzmodells abhängen. Im letzten Teil der Arbeit werden zwei der Ersatzmodelle mit dem Benchmarkmodell bezüglich ihrer Nutzbarkeit zur Datenwertanalyse verglichen. Die verwendete, lokale Methode zur Datenwertanalyse wird auf mehrere plausible Parameterfelder angewandt, um die Nichteindeutigkeit kalibrierter Modellparameter mit einzubeziehen. Mittels dieser erweiterten, robusten Methode werden vorhandene, 'zukünftige' und 'parametrische' Datenwerte in Bezug auf verschiedene Modellvorhersagen für die Ersatzmodelle sowie das Benchmarkmodell evaluiert. Der Vergleich der auf Vorhersageunsicherheiten bezogenen Datenwerte zwischen Benchmarkmodell und dem vereinfachten, physikalisch basierten Modell zeigt, dass dieses Ersatzmodell nur die Ergebnisse für vorhandene Daten korrekt wiedergibt. Das POD Modell dagegen ist ein geeignetes Ersatzmodell hinsichtlich seiner Fähigkeit, Datenwerte des Benchmarkmodells mittels der vorgestellten robusten Methode zu reproduzieren. Dies ist unabhängig von der gewählten Vorhersage und davon, ob vorhandene, 'zukünftige' oder 'parametrische' Daten betrachtet werden.:1 Introduction 2 State of the art 2.1 Surrogate modeling for groundwater systems 2.1.1 Introduction 2.1.2 Projection-based methods 2.1.3 Data-driven methods 2.1.4 Structural simplification methods 2.1.5 Open research questions 2.2 Uncertainty and data worth analysis 2.2.1 Introduction 2.2.2 Sources of uncertainty in groundwater modeling 2.2.3 Types of uncertainty analysis 2.2.4 Data worth analysis 2.2.5 Open research questions 3 Objectives and contributions 3.1 Explicit boundary treatment in POD 3.2 Analysis of model simplification error 3.3 Robust data worth analysis using surrogate models 3.4 Expected impact 4 Methods 4.1 The Wairau Plain aquifer, the complex model and its surrogates 4.1.1 The Wairau Plain aquifer 4.1.2 Complex MODFLOW model of the Wairau Plain aquifer (CM) 4.1.3 Surrogate 1: simplified MODFLOW model (SM1,sMm) 4.1.4 Surrogate 2: linearized POD model (SM2,POD) 4.1.5 Surrogate 3: artificial neural networks (SM3,ANN) 4.2 POD extension for explicit boundary treatment 4.2.1 Groundwater models and basic POD 4.2.2 Theory of explicit treatment of boundary conditions in POD 4.2.3 Different boundary conditions in eb-POD 4.2.4 Cost of eb-POD compared to basic POD 4.3 Model simplification error analysis - theory 4.3.1 A linear model, solution space and null-space 4.3.2 Surrogate model: definition and calibration 4.3.3 Parameter simplification - relationship between complex model and surrogate model parameters 4.3.4 Simplification error of surrogate model predictions 4.4 Model simplification error analysis - scatter plot analysis 4.4.1 Methodology 4.4.2 General features of the scatter plots 4.4.3 Contributions of error terms 4.4.4 Prediction pairs 4.4.5 Summary 4.5 Robust data worth analysis 4.5.1 First-order second-moment uncertainty estimation 4.5.2 Worth of data 4.5.3 Generating calibrated parameter sets - null-space parameter perturbation 4.5.4 Robust data worth analysis 5 Results and discussion 5.1 Explicit treatment of boundary conditions in POD 5.1.1 (Variable) Dirichlet boundaries 5.1.2 Neumann boundaries 5.1.3 Cauchy boundaries 5.1.4 Applying eb-POD: summary 5.2 Quantifying model simplification error 5.2.1 Simplified MODFLOW model: SM1,sMm 5.2.2 POD surrogate model: SM2,POD 5.2.3 ANN surrogate model: SM3,ANN 5.2.4 Surrogate comparison: simplification errors in model predictions 5.3 Robust data worth analysis using surrogate models 5.3.1 Worth of existing data 5.3.2 Worth of 'future' data 5.3.3 Worth of 'parametric' data 5.3.4 Data worth with surrogate models: summary 5.4 Discussion 6 Conclusions and outlook A Appendix: Publications

Environmental risk assessment of chemical substances in the European Union is based on a harmonised scheme. The required models and parameters are laid down in the Technical Guidance Document (TGD) and are implemented in the EUSES software. An evaluation study of the TGD exposure models was carried out. In particular, the models for estimating chemical intake by humans were investigated. The objective of this study was two-fold: firstly, to develop an evaluation methodology, since no appropriate approach is available in the scientific literature. Secondly, to elaborate applicability and limitations of the models and to provide proposals for their improvement. The principles of model evaluation in terms of quality assurance, model validation and software evaluation were elaborated and a suitable evaluation protocol for chemical risk assessment models was developed. Quality assurance of a model includes internal (e.g. an investigation of the underlying theory) and external (e.g. a comparison of the results with experimental data) validation, and addresses the evaluation of the respective software. It should focus not only on the predictive capability of a model, but also on the strength of the theoretical underpinnings, evidence supporting the model?s conceptualisation, the database and the software. The external validation was performed using a set of reference substances with different physico-chemical properties and use patterns. Additionally, sensitivity and uncertainty analyses were carried out, and alternative models were discussed. Recommendations for improvements and maintenance of the risk assessment methodology were presented. To perform the software evaluation quality criteria for risk assessment software were developed. From a theoretical point of view, it was shown that the models strongly depend on the lipophilicity of the substance, that the underlying assumptions drastically limit the applicability, and that realistic concentrations may seldom be expected. If the models are applied without adjustment, high uncertainties must inevitably be expected. However, many cases were found in which the models deliver highly valuable results. The overall system was classified as a good compromise between complexity and practicability. But several chemicals and classes of chemicals, respectively, with several restrictions were revealed: The investigated models used to assess indirect exposure to humans are in parts currently not applicable for dissociating compounds, very polar compounds, very lipophilic compounds, ions, some surfactants, and compounds in which metabolites provide the problems and mixtures. In a strict sense, the method is only applicable for persistent, non-dissociating chemicals of intermediate lipophilicity. Further limitations may exist. Regarding the software, it was found that EUSES basically fulfils the postulated criteria but is highly complex and non-transparent. To overcome the inadequacies a more modular design is proposed.

Background Cancer is one of the leading causes of death in Sweden. In order to develop better treatments against cancer we need to better understand it. One area of special interest is cancer metabolism and the metabolic fluxes. As these fluxes cannot be directly measured, modeling is required to determine them. Due to the complexity of cell metabolism, some limitations in the metabolism model are required. As the TCA-cycle (TriCarboxylic Acid cycle) is one of the most important parts of cell metabolism, it was chosen as a starting point. The primary goal of this project has been to evaluate the previously constructed TCA-cycle model. The first step of the evaluation was to determine the CI (Confidence Interval) of the model parameters, to determine the parameters’ identifiability. The second step was to validate the model to see if the model could predict data for which the model had not been trained for. The last step of the evaluation was to determine the uncertainty of the model simulation. Method The TCA-cycle model was created using Isotopicaly labeled data and EMUs (ElementaryMetabolic Units) in OpenFlux, an open source toolbox. The CIs of the TCA-cycle model parameters were determined using both OpenFlux’s inbuilt functionality for it as well as using amethod called PL (Profile Likelihood). The model validation was done using a leave one out method. In conjunction with using the leave on out method, a method called PPL (Prediction Profile Likelihood) was used to determine the CIs of the TCA-cycle model simulation. Results and Discussion Using PL to determine CIs had mixed success. The failures of PL are most likely caused by poor choice of settings. However, in the cases in which PL succeeded it gave comparable results to those of OpenFLux. However, the settings in OpenFlux are important, and the wrong settings can severely underestimate the confidence intervals. The confidence intervals from OpenFlux suggests that approximately 30% of the model parameters are identifiable. Results from the validation says that the model is able to predict certain parts of the data for which it has not been trained. The results from the PPL yields a small confidence interval of the simulation. These two results regarding the model simulation suggests that even though the identifiability of the parameters could be better, that the model structure as a whole is sound. Conclusion The majority of the model parameters in the TCA-cycle model are not identifiable, which is something future studies needs to address. However, the model is able to to predict data for which it has not been trained and the model has low simulation uncertainty.