An Introduction to Multi-hazard Risk Interactions Towards Resilient and Sustainable Cities

The relationship between disaster resilience and sustainability in the context of urban risk has gained significant attention in recent years as the research and technical community work towards a safer, more sustainable way of living. Urban risk is a complex matrix that involves multiple elements at risk, hazards, temporal scales, and vulnerabilities, and this is why traditional risk assessment approaches that focus on addressing the impacts of a single hazard are inadequate for effectively assessing and managing urban risk, particularly in the current climate change context. With this in mind, the present chapter provides an introduction to the concept of multi-hazard risk and its relevance to resilient and sustainable cities by listing and briefly discussing the types of natural hazards that impact cities the most and examining the importance of risk assessment and management in reducing the risks posed by these hazards. The chapter also explores strategies for building resilience in cities, including the strengthening of physical infrastructure and the enhancement of social and economic resilience, and concludes by discussing future directions for research and practice in multi-hazard risk management for resilient and sustainable cities.info:eu-repo/semantics/publishedVersio

Social Vulnerability in the Lisbon Metropolitan Area

The manifestation of a hazardous process in a given location is clear evidence of a threat to individuals and communities. Without hazard, there is no risk. Vulnerability, however, plays a less evident role in explaining the losses that are observed in databases, whether global or local. Social vulnerability, in particular, represents the underneath conditions that turn individuals and communities more or less able to endure the impacts of hazardous events. A detailed-level analysis of social vulnerability was performed in the Lisbon Metropolitan Area, considering the dimension of the individuals’ characteristics—that we define as criticality—and the characteristics of the surrounding territories in the ability to provide support during and timely recovery after the event—that we define as support capability. The study area is highly contrasting in terms of this later dimension, with urban areas concentrating most of the services and equipment that reduce vulnerability. Regarding criticality, the methodology allowed to identify very-localized hotspots laid out to high propensity to losses from two drivers: employment and education (first principal component of criticality) and age, gender, and old urban fabric (second principal component). Analysed separately or combined in a single social vulnerability index, this information is useful in the planning of short-term actions in the strict field of civil protection operations and in mid- to long-term actions considering a wider perspective of risk governance, bringing to the table public policies in the areas of social care, mobility, urban planning, education, and health services, that address the very deep roots of vulnerability.info:eu-repo/semantics/publishedVersio

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

[EN] Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy gen- eration and market prices variation. Risk assessment and management as integrated part of actual market ne- gotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players’ strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players’ behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company’s scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company’s evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from com- petitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players’ actions according to the participation risk

Improving the Multi-Brand Channel Distribution of a Fashion Retailer

One has seen exponential growth in the number of clients and in the quantities ordered in the fashion retailing multi-brand channel. It has, therefore, become essential to improve the channel’s distribution process in order to meet the customers’ orders in the shortest time, and in a cost-effective manner, thus complying with the delivery terms agreed upon with the market. To this end, one studied aspects such as the mapping of the supply flow process, the occupation of space and the spaghetti-dash diagram of four current distribution process activities. Besides these, one also analyzed the calculation of productivity, cycle times, takt time, as well as the service level designed, with the purpose of preparing a system to evaluate company performance. In addition to these studies, one resorted to the ABC and SWOT customers’ analyses in order to develop the improvement proposal, which was characterized by: (a) changes in the layout, (b) improvements in the supply flow, (c) implementation of gravity carriers, as well as more ergonomic forms of transport, and (d) the use of computer applications developed in Visual Basic language for the distribution process. Based on this proposal, one succeeded in increasing the amount sorted out by the distributor in an eight-hour shift to 294 articles (11,23%). Cycle time was reduced from 0,015 minutes/article to 0,013 minutes/article (13,33%), which allows for the segregation of articles in time for the next collections. In addition, the occupied space was reduced to 47 m2 on average per collection (1,39%), which is translated into a reduction of 1 498 468 meters (23,34%) in the average distance covered per collection. Furthermore, the number of workers was reduced, on average, by five employees (12,82%) per collection. The storage capacity of the finished product was also increased by 535 boxes (11,30%). The total investment needed to achieve these changes is established as being 23 754,42 €; yet, the payback time involved will only be six months, resulting in a cumulative profit of 84 504,23 € by the end of the fall/winter 2020 collection.info:eu-repo/semantics/publishedVersio

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.Os mercados de electricidade sofreram um processo de reestruturação que originou um aumento considerável da competitividade neste sector e, consequentemente, criou novos desafios na operação das entidades nele envolvidas. De forma a ultrapassar estes desafios é essencial para os profissionais uma compreensão detalhada dos princípios destes mercados e de como gerir os seus investimentos num ambiente tão dinâmico e competitivo. A crescente necessidade de entender estes mecanismos e a forma como a interacção das entidades envolvidas afecta os resultados destes mercados levou a uma grande procura de ferramentas de software, nomeadamente simulação, para analisar possíveis resultados de cada contexto de mercado para as várias entidades participantes. Os sistemas multi-agente são adequados à análise de sistemas dinâmicos e adaptativos com interacções complexas entre os seus constituintes, e portanto, várias ferramentas de modelação dirigidas para o estudo dos mercados reestruturados de electricidade usam este tipo de técnicas. Tirando partido destes simuladores, é possível estudar vários tipos de mercados e a interacção entre as entidades neles envolvidas. No entanto, todos estes simuladores apresentam lacunas no que diz respeito ao apoio à decisão a essas entidades, nomeadamente na gestão dos seus investimentos. Um aspecto tão relevante como é a utilização de todo este suporte de simulação para permitir aos agentes de mercado realmente aprenderem com a experiência de mercado e desenvolveram capacidades para analisar contextos de negociação e adaptar automaticamente os seus comportamentos estratégicos de acordo com as circunstâncias, não é considerado na amplitude que é requerida. É neste âmbito que esta dissertação contribui, utilizando técnicas de inteligência artificial para oferecer um apoio relevante e eficaz às decisões estratégicas das empresas envolvidas nestes tipos de negociação. O principal objectivo deste trabalho é dotar essas entidades de capacidades que lhes permitam apresentar comportamentos inteligentes e adaptativos na sua actuação nos mercados de electricidade de forma a serem capazes de atingir os seus objectivos da melhor forma possível, sendo capazes de reconhecer e actuar em conformidade com os contextos em que estão inseridas. De forma a atingir este objectivo, foi desenvolvido o sistema ALBidS – Adaptive Learning strategic Bidding System (sistema de aprendizagem adaptativa para licitações estratégias). Este sistema está implementado como um sistema multi-agente independente, em que cada agente é responsável pela execução de uma abordagem estratégica diferente. Este sistema está integrado com o simulador MASCEM, para que seja possível testar e validar as contribuições dadas num contexto de simulação de mercados já implementado e consolidado. Sendo este simulador uma ferramenta que simula mercados de electricidade permitindo a utilização de informação obtida a partir de mercados de electricidade reais, garante-se, assim, também que as conclusões retiradas deste trabalho são apoiadas por experimentação baseada em casos reais ou quase reais. A definição das estratégias de oferta dos agentes de mercado é baseada na aprendizagem adaptativa por parte das entidades, considerando o histórico do sistema, através da informação disponível, incluindo informação recolhida durante a utilização do próprio sistema multi-agente. Para isso são propostos e testados vários algoritmos e metodologias de aprendizagem e análise de dados, para que conjuntamente contribuam para que os agentes possam tomar as melhores decisões em cada momento de acordo com o contexto identificado. Um contributo importante do trabalho está na proposta destes algoritmos, na sua combinação e na obtenção de conhecimento relativo à utilização criteriosa dos algoritmos considerados em função do contexto, utilizando o conceito de context awareness. A análise destes contextos é efectuada por um mecanismo desenvolvido para esse efeito, analisando as características específicas de cada dia e período de negociação. São estudados e analisados vários algoritmos baseados em abordagens diversas, para que seja possível contemplar formas distintas de resolver problemas, dependendo de circunstâncias concretas. Entre estas abordagens, podem referir-se: redes neuronais artificiais dinâmicas; teoria de jogos; médias/regressões lineares; abordagens económicas, tendo em conta a análise macroeconómica e sectorial, e também a análise interna das empresas no que diz respeito aos seus investimentos e perspectivas de crescimento; algoritmos de Inteligência Artificial (IA), como os algoritmos Roth-Erev e o Q-Learning de aprendizagem por reforço; uma abordagem baseada na teoria do determinismo, em que são analisadas todas as variáveis intervenientes na obtenção dos resultados pelo simulador; e outras propostas de algoritmos de aprendizagem e análise de dados específicos para determinadas situações, bem como a combinação de algoritmos de tipos diversos. Numa camada superior aos algoritmos mencionados foi implementado um mecanismo de aprendizagem por reforço, baseado em estatísticas e em probabilidades, que é responsável por escolher em cada altura a proposta de licitação que dá mais garantias de sucesso. Com o passar do tempo, vão sendo actualizadas as estatísticas, através da análise dos resultados de cada proposta. Este mecanismo permite que em cada momento sejam escolhidos os algoritmos que estão a ter os melhores resultados para cada situação e contexto. Ao serem considerados vários algoritmos, de naturezas completamente distintas, consegue-se uma maior probabilidade de haver sempre algum a oferecer bons resultados. Existe também a possibilidade de se definir as preferências e parametrizações relativas a cada algoritmo individualmente, e também de se definirem preferências relativas ao desempenho dos algoritmos no que diz respeito à eficiência computacional, permitindo que o utilizador escolha a relação eficiência/probabilidade de sucesso, de acordo com as suas preferências. O sistema excluirá então, automaticamente, os algoritmos que usualmente requerem um maior tempo de processamento, quando esse tempo não corresponde a soluções proporcionalmente melhores. Desta forma, garante-se que o sistema estará a utilizar o seu tempo de processamento em abordagens que oferecem melhores respostas no menor tempo possível. Como apoio ao funcionamento adequado das estratégias implementadas foi criado um mecanismo de definição de perfis dos agentes competidores. Desta forma é possível obter previsões acerca das acções esperadas dos outros agentes participantes no mercado, tendo em conta as suas acções passadas e as reacções verificadas quando confrontados com situações específicas, como o sucesso ou o falhanço

Flood Risk Assessment in the Lisbon Metropolitan Area

Flood processes are one of the most challenging to risk assessment and management. In many situations, peak flows are generated kilometers away from the places where inundation is observed. Scale in flood risk assessments is a fundamental factor when estimating hazard, exposure, and vulnerability. Municipal, civil parish, and building-level information are used to construct flood risk indexes and profiles. It is observed that, depending on the scale at which it is represented, the same root information provides distinct insights into flood risk expression in the Lisbon Metropolitan Area. When compared with the Flood Directive critical areas, the results show they are mostly consistent with the results at the different scales, identifying the same hotspots of flood risk (in the Loures, V. F. Xira, and Setúbal municipalities) as those selected during the Directive’s implementation. Flood loss reduction implies the involvement of distinct risk practitioners and decision-makers, acting at distinct scales and sectors related to risk governance. Interconnections between flood risk components and between flood processes and other potential cascading processes are still insufficiently known and require the priority of society.info:eu-repo/semantics/publishedVersio

Identification of honey bee populations from the Azores: insights from wing geometric morphometrics

The geometric morphometrics of the wings has been an important method for the identification and evaluation of honey bee diversity patterns around the world. Honey bee populations of the Macaronesian archipelagos of Canaries and Madeira have been intensively surveyed for diversity using a variety of genetic markers. In contrast, honey bee populations inhabiting the Azorean archipelago have been largely undersampled. To fill this gap, we sampled 473 colonies from across the Azores and assessed diversity patterns using a geometric morphometrics approach. A total of 5 forewings were collected per colony, mounted in a slide and photographed with a stereomicroscope. Additionally, the forewings representing 711 colonies of A. m. iberiensis, 11 A. m. ligustica, 15 A. m. carnica and 12 A. m. caucasia were used as reference samples. To extract shape information, 19 anatomical landmarks were plotted across the veins’ intersections in the wing structures of all individuals. The analyses of wing shape were performed in MorphoJ using the Procrustes superimposition method. Shape differences were investigated through multivariate statistical analysis and Mahalanobis and Procrustes distances were used to construct a dendrogram of the morphological proximity. Results revealed the power of landmark-based methods to discriminate different honey bee populations from the Azores, and also to distinguish them from the subspecies of the reference collection. The wing geometric morphometrics patterns showed that while, overall, populations from the Azores exhibited a closer relationship with A. m. iberiensis, some populations, especially those from the islands of Graciosa, but also Terceira and Pico tended to cluster closer to A. m. ligustica, A. m. carnica. Several non-mutually exclusive factors can contribute to the observed wing patterns such as the recent human-mediated introductions of subspecies from Eastern Europe, and the founder effect resulting from honey bee introductions in historical times. Moreover, the particular insular environment and the barrier to gene flow due to geographical isolation possibly shaped the diversity patterns currently observed in the Azores.info:eu-repo/semantics/publishedVersio