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Insurance companies are grappling with the rising frequency and severity of extreme weather-related flooding events, which currently pose the highest financial burden both in total and per individual case. The existing insurance model isn't economically sustainable if such events continue to increase. To assess future needs and challenges in flooding insurances, research on changing weather patterns and interviews with employees at major firms were conducted. The research indicates that climate change has and will further worsen extreme rain events in Sweden, leading to more frequent and intense flooding events. Interviews revealed that firms are aware of impending changes in the insurance industry due to climate change but lack proactive measures to address them. Responsibility is fragmented, and communication between stakeholders is suboptimal. To address these challenges, I look at recent research regarding flood risk assessment and if these are applicable for the insurance industry in Sweden to ensure future profitability.

Climate change is projected to increase the intensity and frequency of storm events that would increase flooding hazards. Urbanization associated with land use and land cover change has altered hydrological cycles by increasing stormwater runoff, reducing baseflow and increasing flooding hazards. Combined urbanization and climate change impacts on long-term riparian flooding during future growth are likely to affect more socially vulnerable populations. Growth strategies and green infrastructure are critical planning interventions for minimizing urbanization impacts and mitigating flooding hazards. Within the social-ecological systems planning framework, this empirical research evaluated the effects of planning interventions (infill development and stormwater detention) through a risk assessment in three studies. First, a climate sensitivity study using SWAT modeling was conducted for building a long-term flooding hazard index (HI) and determining climate change impact scenarios. A Social Vulnerability Index (SoVI) was constructed using socio-economic variables and statistical methods. Subsequently, the long-term climate change-induced flooding risk index (RI) was formulated by multiplying HI and SoVI. Second, growth strategies in four future growth scenarios developed through the BMA ULTRA-ex project were evaluated through land use change input in SWAT modeling and under climate change impact scenarios for the effects on the risk indices. Third, detention under climate sensitivity study using SWAT modeling was investigated in relation to long-term flooding hazard indices. The results illustrated that increasing temperature decreases HI while increasing precipitation change and land use change would increase HI. In addition, there is a relationship between climate change and growth scenarios which illustrates a potential threshold when the impacts from land use and land cover change diminished under the High impact climate change scenario. Moreover, spatial analysis revealed no correlation between HI and SoVI in their current conditions. Nevertheless, the Current Trends scenario has planned to allocate more people living in the long-term climate change-induced flooding risk hotspots. Finally, the results of using 3% of the watershed area currently available for detention in the model revealed that a projected range of 0 to 8% watershed area would be required to mitigate climate change-induced flooding hazards to the current climate conditions. This research has demonstrated the value of using empirical study on a local scale in order to understand the place-based and watershed-specific flooding risks under linked social-ecological dynamics. The outcomes of evaluating planning interventions are critical to inform policy-makers and practitioners for setting climate change parameters in seeking innovations in planning policy and practices through a transdisciplinary participatory planning process. Subsequently, communities are able to set priorities for allocating resources in order to enhance people's livelihoods and invest in green infrastructure for building communities toward resilience and sustainability

Electric power system (EPS) is an integral part of infrastructure systems. Ensuring its resiliency to extreme weather events and natural hazards is crucial to protect the safety, economy and public health. Recorded and projected data show an increase in the frequency and severity of extreme weather events and natural hazards attributed to a changing climate. It is critical to ensure the integrity of the aging infrastructure systems and to promote environmental justice by shrinking the energy-equity gap to lower power outages in disadvantaged communities. An important aspect is the resiliency interdependency of EPS to other critical infrastructure systems, an aspect that has been escalating due to rapid urbanization and technological developments. The main objective of this research is to quantitatively evaluate the resilience of levee-protected power grid to flooding in a changing climate and adapting a strategy to enhance the resilience of power grid. Thus, this study first establishes a methodological and multi-disciplinary framework by integrating climate science, hydrology, and EPS analysis to study (I) how climate change affects recurrence intervals of flooding, (II) how the integrity of levees will be affected by changes in flooding patterns, (III) how these changes affect the resilience of an EPS located in levee-protected areas, and (IV) how to improve the resilience of the EPS while reducing the energy-equity gap. The proposed framework is applied to some IEEE standard test systems overlaid on a levee-protected area in Northern California. First, a link-based resiliency analysis is performed using the direct current optimal power flow (dc-OPF) method applied to the IEEE-24 standard test system. Then, a node-based resiliency analysis is carried out employing the IEEE 118-bus test system. The system resiliency is assessed for pre-flooding, historic flooding, and projected future flooding scenarios using two representative climate pathways (RCP). Finally, an optimal adaptation strategy using the placement of distributed energy resources (DERs) is delineated using a modified IEEE 30-bus test system to reduce flooding-induced power outages, prioritizing disadvantaged communities by minimizing energy inequity among the communities. Results of this study reveal that the adaptation plan can reduce the risk of power outages, improve environmental justice and the resilience of power networks. The findings of this study can contribute towards more resilient EPS under a changing climate.

Due to the global warming and climate change, an increased frequency of high intensity rains and other disasters are expected all around the world. To predict this change in climate the IPCC has created a set of climate scenarios, RCPs, that will try to predict the future climate based on how much we are able to adapt and mitigate the effects we as a species have on the environment. This master thesis will seek to explore the possibility to use urban planning tools to help mitigate the increased effects and sizes of floods due to the global warming. To achieve this, a case study of Luleå is done, where urban indicators are used to improve the resilience of the city. This is combined with a policy study to see how Luleå compare to other cities in their policies that affect flood protection and mitigation. To have a strong flood protection system in a city several factors need to be considered. One of the biggest factors is what type of strategy is chosen. One possible strategy is resilience, this combines seeming paradoxes into a working flood protection and mitigation plan. Resilience improves the flood protection and mitigation by combining and improving the robustness, adaptability, and transformability of the city. This is done by, among other things promoting inter disciplinary cooperation, public cooperation and knowledge of flooding, and promoting the use of water in the city as an asset. Blue and green infrastructure could also be implemented into the city as these measures help improve the resilience of a city in many regards. Not just for flood protection, but it can also help mitigate the effects of droughts or heatwaves and improve the general wellbeing of the citizens. In the policy study it was found that different cities varied in both scale and strategy in their flood protection measures. All the cities that were looked at would also need to increase the scale of their protection and mitigation measures to mitigate the increased size and frequencies that the climate change brings. In Sweden, especially in the northern parts, the increased risk is not as high as in other parts of Europe. This is due to the land rise in Sweden mitigating the sea level rise. In Luleå the sea level and land rise are expected to fully mitigate each other until the year 2100. The policy study also showed that a history of flood related disasters did not necessary guarantee a strong flood protection scheme, but it would increase the probability of one. In the case of Luleå, the city has mostly focused on flood proofing buildings and infrastructure in the high-risk areas or those who are seen as critical to the society. Based on the analysis of the policies and indicators that were developed for Luleå, the city seems to have good protection from the current risks, such as a 100-year flood, flow, or rain. But the systems in place will most likely need to be expanded and developed further to mitigate the rising risk due to global warming. Some measures that can be implemented are related to the adaptability and transformability, like brochures that teaches the public about flood-protection and what to do and how to act in case of a large flood in the city.

Background:Software-Defined Networking (SDN) represents a network architecture that offers a separate control and data layer, facilitating its rapid deployment and utilization for diverse purposes. However, despite its ease of implementation, SDN is susceptible to numerous security attacks, primarily stemming from its centralized nature. Among these threats, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks pose the most substantial risks. In the event of a successful attack on the SDNcontroller, the entire network may suffer significant disruption. Hence, safe guarding the controller becomes crucial to ensure the integrity and availability of the SDN network. Objectives:This thesis focuses on examining the IP spoofing attack and its impact on the Data Plane, particularly concerning the metrics of an SDN switch. The investigation centers around attacks that manipulate flow-rules to amplify the number of rules and deplete the resources of a switch within the Data Plane of an SDN network. To conduct the study, a software-defined network architecture was constructed using Mininet, with a Ryu controller employed for managing network operations. Various experiments were carried out to observe the response of the SDN system when subjected to an IP spoofing attack, aiming to identify potential mitigation strategies against such threats. Method and Results: To simulate the resource exhaustion scenario on the SDN network’s Data Plane,we deliberately triggered an escalation in the number of flow-rules installed in the switch. This was achieved by sending packets with spoofed IP addresses, there by exploiting the switch’s limited resources. Specifically, we focused on monitoring the impact on CPU utilization, storage memory, latency, and throughput within the switch. Detailed findings were presented in the form of tables, accompanied by graphical representations to visually illustrate the effects of increasing flow rules on the switches. Furthermore, we explored potential mitigation measures by developing an application that actively monitors the flow rules on the Ryu controller, aiming to detect and counteract such resource-exhausting effects.

The world is increasingly becoming urban. In fact, 80 percent of the US population is already living in cities. With the influx of a huge population in urban areas, the urban infrastructures are bound to be stressed. Furthermore, people from every walk of life live in urban areas in search of better economic opportunities. These diverse people have diverse needs. To make matters worse, governments have a limited budget. And, they are faced with the challenge of providing infrastructure and public services fair to everyone. This thesis attempts to respond to these challenges through two manuscripts. The first manuscript proposes a decision-support tool that responds to these challenges along with the flooding vulnerability due to sea-level-rise. As flooding events are getting more frequent and intense, coastal road network is vulnerable and can significantly affect daily mobility. Therefore, the paper proposes an optimization framework that minimizes the cost of mitigation measures for flooding while also considering social equity. As a result, the results of this optimization function is not only financially optimum but also equitable to all. The second manuscript proposes a novel framework for analyzing equity in terms of access to opportunity, rather than equity of outcomes. We showcase the use of a large-scale, high-fidelity agent-based, activity-based travel demand model to produce travel times to employment centers. This travel time is used as a proxy to access to opportunities. The results are visualized in a GIS heatmap. The model is applied to the Metropolitan Washington DC area. This manuscript contributes to the literature by analyzing the equity of opportunities without considering an individual’s socioeconomic characteristics. / Master of Science / The world is increasingly becoming urban. In fact, 80 percent of the US population is already living in cities. With the influx of a huge population in urban areas, the urban infrastructures are bound to be stressed. Furthermore, people from every walk of life live in urban areas in search of better economic opportunities. These diverse people have diverse needs. To make matters worse, governments have a limited budget. And, they are faced with the challenge of providing infrastructure and public services fair to everyone. This thesis attempts to respond to these challenges through two manuscripts. As flooding events are getting more frequent and of more intensity, coastal road network is vulnerable and can significantly affect day-to-day movements. Decision makers face the challenge of mitigating the flood risk under budget constraints and they need to make their decision fair to everyone. The first manuscript proposes a decision-support tool that not only optimizes the use of a limited budget but also ensures the decision is fair to everyone. The idea of what is fair to everyone is a contentious issue. Recently some people have argued against using socioeconomic characteristics of people in making investment decisions. Therefore, the second manuscript proposes a novel framework that analyzes access to employment centers using a higher fidelity advanced travel demand model without the explicit use of socioeconomic characteristics of individuals.

Software Defined Networking (SDN) modernizes network control, offering streamlined management. However, its centralized structure makes it more vulnerable to distributed Denial of Service (DDoS) attacks, posing serious threats to network stability. This thesis explores the development of a DDoS attack prevention technique in SDN environments using the Ryu controller application. The research aims to address the vulnerabilities in SDN, particularly focusing on flooding and Internet Protocol (IP) spoofing attacks, which are a significant threat to network security. The study employs an experimental approach, utilizing tools like Mininet-VM (VirtualMachine), Oracle VM VirtualBox, and hping3 to simulate a virtual SDN environment and conduct DDoS attack scenarios. Key methodologies include packet sniffing and rule-based detection by integrating Snort IDS (Intrusion Detection System), which is critical for identifying and mitigating such attacks. The experiments demonstrate the effectiveness of the proposed prevention technique, highlighting the importance of proper configuration and integration of network security tools in SDN. This work contributes to enhancing the resilience of SDN architectures against DDoS attacks, offering insights into future developments in network security.

River ice breakup is a vital process to the morphology and hydrology of many rivers in Canada, often governing peak flows of the river. These events can occur through multiple mechanisms, with the potential for volatile or early breakup events that can have severe impacts to the river. Ice jam flooding can be a potentially devastating result of river ice breakup while early breakup of ice cover in a mid-winter breakup can be unpredictable and greatly alter the remaining ice season. These events are growing increasingly common as a result of climate change, and as a result there is a need to develop prediction tools for these events to aid in decision making support. Past investigations into developing such tools, especially from a data-driven modelling perspective, are challenged by the availability and complexity of the data related to these rare and dangerous to measure events. Therefore, the goal of this dissertation was to develop and apply methods to address the historical challenges and shortcomings in predicting these events through the use of data-driven modelling techniques. This includes: i) development of a stacking ensemble modelling framework for the prediction of ice jam presence during the spring breakup season of a river, utilising variable selection and rare-event forecasting techniques in combination with a comprehensive selection of machine-learning algorithms; ii) return period and trend analysis of mid-winter breakups in conjunction with comprehensive input analysis techniques to identify the key drivers of these events’ severity and develop a means of classifying the flood risk based on hydroclimatic traits; iii) the development of a two-level modelling system for the prediction of the occurrence and timing of mid-winter breakups on a national scale utilising rare event forecasting techniques and imbalanced learning; and iv) development of a novel hybrid semantic and machine learning modelling system in which an ontology is used in conjunction with network analysis techniques to select variables for machine learning models, which is used on a national case study of the prediction of spring breakup timing in Canada. The results of each study in application to their respective case studies demonstrate the effectiveness of the proposed techniques, which are shown to be easily adaptable to other regions or locations. These techniques can form the backbone of decision-making support for communities on rivers that are affected by the unpredictable and oftentimes volatile nature of river ice breakup. / Thesis / Candidate in Philosophy / River ice breakup is a key event to the hydrology of rivers throughout Canada, playing a major role in their physical and ecological characteristics. The timing and mechanism of these events can, however, be unpredictable and volatile, with the effects of climate change only exacerbating these risks. This dissertation focuses on addressing these potential issues through the application of machine learning and hybrid modeling in the prediction of river ice breakup events. Advanced data driven techniques coupled with novel applications of other analytical methods are used to: i) predict the presence of ice jams through the application of stacking ensemble modelling; ii) predict the severity of mid-winter breakups through application of trend and variable analysis; iii) predict the occurrence and timing of mid-winter breakups using rare-event forecasting techniques; and iv) develop a novel hybrid modelling scheme coupling ontology-based semantic modelling and machine learning to predict spring breakup timing. Detailed case studies for each application are provided demonstrating the effectiveness of the discussed techniques.