Nature-based solutions (NbS) such as urban parks, green corridors, and stormwater wetlands are increasingly adopted to enhance climate resilience and livability in cities, yet their social impacts can be uneven. In Cape Town—where apartheid-era planning left certain communities on the under‑resourced Cape Flats while the wealthier people clustered around greenspaces—there is a pressing need to understand whether greening projects might inadvertently displace vulnerable renters. This thesis examines that question by focusing on two contrasting electoral wards: Ward 57, an affluent inner‑city neighbourhood, and Ward 79, the lower‑income Mitchell’s Plain township in the Cape Flats.
To explore how NbS affect housing affordability and demographic composition, a spatially explicit agent‑based model (ABM) was developed for each ward. The city map is discretized into a grid of housing parcels, each with evolving rent levels and ‘livability’ scores that reflect environmental quality, including the presence and maturity of NbS interventions. Household agents—characterised by income, rent‑affordability thresholds, and demographic identifiers—occupy units and face displacement when rising rents exceed their budget or eviction risk thresholds. Vacant units may then be filled by higher‑income in‑migrants. NbS scenarios are introduced exogenously, locally boosting livability scores and triggering rent uplifts that propagate through the spatial grid. Over multi‑year simulations, the model tracks rent burdens, forced displacement events, and shifts in population makeup, enabling side‑by‑side comparison of greening impacts in both wards under a baseline ‘no‑policy’ scenario and alternative housing‑policy regimes.
Results show that, without housing safeguards, NbS raise rents significantly—by roughly 20–25% over ten years—displacing thousands of low‑income households. In Ward 57, this reinforces existing privilege as affluent renters easily absorb price increases, whereas in Ward 79 it displaces predominantly Black African households and Coloured households, substituting them with higher‑income newcomers. Introducing inclusionary housing requirements—which reserve a substantial share of units near NbS sites for affordable rents, dampens rent inflation, retains most incumbent households, and preserves socio‑economic diversity. Time‑limited rent subsidies yield similar short‑term relief but fail to prevent eventual displacement once support expires.
These findings underscore that NbS, while delivering environmental and recreational benefits, can exacerbate urban inequality unless paired with deliberate affordability measures. To achieve just transitions, Cape Town’s green‑infrastructure initiatives should be coupled with inclusionary zoning, dedicated affordability funds, and robust community engagement processes that empower residents and monitor displacement risks. By integrating climate‑adaptation investments with housing‑equity policies, cities can ensure that ecological solutions do not become drivers of social exclusion

This thesis compares two pathways for decarbonising long-haul freight: Battery Electric Heavy-Duty Vehicles (BEHDVs) using static charging and BEHDVs supported by Electric Road Systems (ERS) enabling dynamic charging. Using the Multi-Actor Multi-Criteria Analysis (MAMCA) and Best-Worst Method (BWM), the study evaluates both options from the perspectives of six stakeholder groups: carriers, shippers, vehicle manufacturers, grid operators, road operators, and freight forwarders.

Stakeholder criteria were identified through eleven expert interviews and weighted via BWM surveys, then linked to measurable, normalised indicators. Results show BEHDV-only is preferred by manufacturers and freight forwarders, while ERS is favoured by grid and road operators for its grid load balancing and land use efficiency. Shippers and carriers show moderate ERS preference, citing reliability and driver well-being benefits.

A phased strategy, static charging for near-term deployment and targeted ERS rollout on high-utilisation corridors, is recommended to balance operational, economic, and long-term sustainability goals.

Climate change poses a growing threat to critical infrastructure systems, particularly road networks, which are vulnerable to flooding, heat waves, and extreme weather events. Despite the increasing urgency to adapt infrastructure to these risks, assessing suitable adaptation measures remains a complex challenge due to competing priorities, including effectiveness, cost, and social impact. This thesis aims to help decision-makers assess suitable climate change adaptation measures for road infrastructure. The research employs Multi-Criteria Decision Analysis (MCDA) tools, namely the Best-Worst Method (BWM), to derive weights for the evaluation criteria based on stakeholder preferences, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the adaptation alternatives accordingly. A survey was conducted and received 129 responses from academic, governmental, and practitioner backgrounds across the Global North and South.

This study found that respondents assign higher importance weight to adaptation strategies that effectively reduce disaster risk (28%), have minimal unintended consequences (27%), and have low adverse societal impact (27%), placing less emphasis on project cost (18%). As a consequence, Improved Drainage, Vegetation Plans, and Monitoring Systems emerged as the most suitable adaptation measures. In contrast, Alternative Roads, Road Reallocation, and Road Height adjustments were seen as less favorable. Subgroup and sensitivity analyses confirmed the robustness of these findings, showing only minor variations across respondent backgrounds and regions. Despite limitations such as a modest sample size and limited regional specificity, this study can be a reference for policymakers in assessing suitable climate change adaptation strategies in road infrastructure through criteria-based evaluation. The assessment approach is universally applicable and can be extended to other public infrastructure sectors such as energy, transportation, and buildings.

This study analyses the effectiveness of different recovery strategies for transport systems after disruptions, with special attention to the influence of network topology and recovery objectives. By means of simulations on four networks with different structural characteristics (central, radial, decentralized and ring-radial) it is shown that no single strategy is universally applicable. The performance of recovery strategies such as proximity, hierarchy, recovery time and dynamic recovery appears to be strongly dependent on both the degree of fragmentation and the specific structure of the network. Furthermore, the optimal approach differs per functional recovery objective, such as accessibility, efficiency, resilience or robustness. The study therefore not only provides scientific insights into network recovery, but also practical tools for policymakers and planners who have to make decisions under time pressure about recovery after infrastructure disruptions.

Currently, the number of refugees is increasing, while the effects of climate events are worsening. Given this context, it is becoming increasingly important to understand the potential relationships between refugee movements and climate exposure, particularly at the origin and destination locations of refugees. This understanding is crucial for predicting future movements of climate-related refugees and identifying effective climate adaptation measures at both origin and destination locations.

Research on the global relationships between refugee movements and climate exposure is lacking in the current literature, especially in terms of data-driven, analytical approaches. This research addresses this gap.

The main research question of this thesis is: How is climate exposure associated with global refugee movements? To answer this, a literature review and data analytics are employed. The literature review identifies climate-related hazards that serve as inputs for the data analysis. The hazards considered in this study include droughts, sea-level rise, coastal flooding, riverine flooding, and cyclones. Various types of (possible) associations are examined using correlation analyses, regression analyses, spatial cluster identifications, and bivariate choropleth maps. All analyses consider changes over the years, specifically from 2003 to 2022.

As one of the results, this research presents an equation for relatively accurate predictions of refugees fleeing from countries in recent years, based on climate indicators while also incorporating social factors.

Moreover, the results of the study indicate that sea-level rise and riverine flooding are significantly associated with refugees fleeing from a country. Future research should explore whether this relationship could potentially be causal.

Additionally, the study identifies hotspots for different climatic conditions as well as for refugee movements (both fleeing to and from countries). The hotspots for climatic indicators and refugee movements align relatively well. Generally, climate exposure and large refugee movements are concentrated in Central-East Africa, the Middle East, and South-East Asia. These hotspots have not changed significantly over the last 20 years.

Lastly, the thesis compares top origin-destination locations of refugees at both generic (statistical) and individual (visual) levels. While statistical differences between top origin and destination locations are sometimes inconclusive, individual comparisons of countries within the same regions provide key insights into the differences in climatic exposure for refugees.

More effective climate adaptation measures can be implemented at refugee destination locations by tailoring them to specific climatic exposures, taking into account the conditions at their origin locations. Specific investments regarding adaptation measures have been identified for particular regions and countries, and these are recommended to policymakers.

The procedures and tests developed in this research can be applied to similar studies using different or newer climate and refugee data.

The increasing impacts of climate change are presenting numerous challenges globally, with one of the most pressing being the heightened risk of flooding due to rising sea levels and more intense rainfall patterns. However in many countries, among which the Netherlands, a significant portion of residential properties are at risk of flooding—a risk that is often underappreciated or overlooked by homeowners. The primary focus of this thesis is to explore the potential introduction of a flood risk disclosure policy, referred to as the "Climate Label," and its implications for real estate development practices.
By making flood information readily available, the policy aims to influence market values and guide investment decisions, potentially encouraging more resilient housing developments. The central question of this study examines how such a policy might affect the decision-making processes of real estate developers and, consequently, the distribution of flood risks across new residential constructions.

The exploration of this topic is timely and significant, given the scarcity of existing literature on the impact of flood risk information policies on housing supply and developer behavior. Real estate developers, particularly those in the private sector, play a crucial role in the construction of new housing and their profit-driven motives make their responses to such policies particularly insightful for understanding broader market dynamics.

This research employs an Agent-Based Modeling (ABM) approach to simulate the interactions within the housing market, developer decision-making, and assess the potential outcomes of implementing the Climate Label. Moreover, interviews with real-estate developers are conducted to achieve a deeper understanding of their decision-making, their current perspective of building flood-resilient housing, and how a Climate label would influence such.

The model results it can be concluded that a housing value shift due to flood risk disclosure can cause a change in the development patterns of new residential houses. However, the distribution of flood risks is highly dependent on the magnitude of the price shift, whether developers perceive it as a gain or loss, and the reference point they use in their project decision-making to assess gains and losses.

This master's thesis investigates the complex interdependencies between mobility and electricity sectors in the Dutch energy transition (2016-2050). Through comprehensive system dynamics modeling and scenario analysis, the research examines how Battery Electric Vehicle (BEV) adoption patterns interact with renewable energy integration. The study develops three major scenario analyses: exploring various BEV adoption rates combined with energy sector transitions, investigating different renewable energy generation capacities (120-180 TWh), and evaluating the feasibility of the Netherlands' 2030 zero-emission vehicle sales mandate. The findings demonstrate that while renewable energy capacity can accommodate increased electric vehicle demand, successful transition requires careful cross-sectoral coordination. The research contributes to understanding critical infrastructure challenges and provides valuable insights for policymakers and stakeholders involved in sustainable infrastructure development, particularly in the Dutch context.

Purpose of the Study
This thesis addresses the growing challenge of urban heatwaves by developing a decision-support system (DSS) called NBScenarios, which helps policymakers implement Nature-Based Solutions (NBS) to mitigate urban heat island effects. This study focuses on Paris as a study case, with the overarching goal to propose different options that optimize the configuration of green roofs and trees along streets (replacing parking spaces) to effectively reduce urban heat islands. These configurations balance differently goals that are challenging to reconcile: maximizing the cooling effect, respecting budgetary constraints, prioritising adaptation benefits to the most vulnerable population, while guaranteeing access for all urban citizens to cool spaces.

Problem Statement
Despite the potential of Nature-Based Solutions for urban climate adaptation, their implementation is blocking at the decision-making stage. Existing decision-support systems (DSS) often lack social considerations, limiting their real-world effectiveness. Many DSS are not adaptable across diverse urban contexts and do not integrate multi-objective optimization, which is essential for balancing conflicting priorities like costs and environmental benefits. Moreover, DSS are often complex, limiting stakeholder engagement. This highlights the need for modular, expandable tools that support informed, inclusive decision-making.

Research Approach
This thesis employs a multi-objective optimization (MOO) approach, utilizing the ε-NSGA-II algorithm and integrating spatiality. The selection and definition of case-relevant nature-based solutions, objectives, and constraints, is structured by the XLRM framework, in particular. A spatial grid (100m x 100m cells) covering Paris allows for detailed urban data to be processed and integrated into the DSS. The DSS generates a range of contrasted Pareto-optimal solutions, balancing trade-offs among objectives and corresponding NBS mapping, providing policymakers with a diverse range of options.

Key Findings
The DSS produced a range of optimal and diverse options for NBS implementation in Paris, with green roofs and on-street parking transformed into street trees. Results showed typical values for cooling effects ranging from 0°C to 7.5°C, initial costs spanning from €500 million to €3.5 billion, and clear trade-offs between maximizing cooling and minimizing costs. Solutions effectively revealed the spatial distribution of cooling impacts and highlighted disparities between areas with vulnerable populations and high cooling potential.

Conclusion
NBScenarios provides a flexible and adaptable framework for policymakers to balance environmental, economic, and social objectives in urban climate adaptation. While the study focuses on Paris, the DSS is designed to be applied to other cities facing similar climate challenges, allowing for scalability and adaptability. The findings highlight the system's ability to inform critical policy decisions related to heatwave mitigation, including cost considerations and social justice factors, facilitating trade-offs between competing priorities.

Recommendations
For future work, the DSS could incorporate uncertainties and include other NBS types or objectives. Continued testing in different urban contexts is also recommended to refine the system and ensure its broader applicability. The creation of an interactive dashboard, based on the DSS current output, could further improve stakeholder engagement and decision-making.

Significance of the Study
This research advances urban climate adaptation by integrating multi-objective spatial optimization into decision-support systems for nature-based solutions. Academically, it addresses social factors and NBS mainstreaming while offering a modular, interdisciplinary DSS adaptable to other cities. Societally, the study enhances policy-making and stakeholder engagement, emphasizing social justice in climate strategies. The DSS enables cities globally to scale NBS effectively, bridging gaps between technical design and actionable, socially responsible urban resilience policies.

Flooding, one of the costlier climate change disasters, has emerged as a pressing global challenge, growing in frequency and severity. The traditional reliance on government intervention alone to protect households from flooding is not enough. An essential shift in perspective underscores the need for households themselves to become proactive participants in multi-level protection strategies. To achieve this, drivers of vulnerability shaping household adaptation to flooding are explored, focusing on social, physical and perceived vulnerabilities. Furthermore, examining these vulnerabilities on a local scale is crucial and a knowledge gap, as it is at this level that household adaptation predominantly occurs.

Recognising the varying manifestations of these vulnerabilities depending on the flood-prone location, this study prioritises the examination of disparities in vulnerability profiles between two representative cities: Jakarta, representing the Global South, and Houston, representing the Global North. By doing so, this research contributes significantly to the ongoing scientific discourse on climate change adaptation, as well as assist in extrapolating gained insights to data-scarce regions. This research aims to assists in aligning flood management efforts more closely with the needs and concerns of the residents in each city, acknowledging the distinct perceptions of flood hazards and socio-economic disparities. This, in turn, lays the groundwork for the development of targeted support and interventions, fostering more inclusive and resilient communities capable of mitigating the impacts of flooding and adapting to future challenges. The primary research question driving this study is as follows: 'How are social, physical and perceived vulnerabilities that influence flood adaptation different among households in an urban space?' To answer this question comprehensively, the research employs a multi-faceted approach, examining each vulnerability dimension separately before exploring their interactions in the urban context.

Comparing the social, physical and perceived vulnerability maps for Jakarta in the context of flooding uncovered many discrepancies and complexities that contribute to the scientific debate surrounding flood adaptation. The disparities between these dimensions highlight the need for a comprehensive approach to vulnerability assessment and flood mitigation. By recognising that vulnerability is not solely determined by physical factors, but also influenced by social dynamics and individual perceptions, authorities can develop strategies that foster community resilience and enhance disaster preparedness. Therefore, this research recommends addressing mismatches in risk perception, understanding the nuanced distribution of vulnerabilities and implementing context-specific interventions in order to build a safer and more resilient world in the face of flooding and other environmental challenges.

The shifting global climate patterns present an imminent threat to human existence. Addressing this critical issue necessitates decisive action in multiple sectors, including the mobility service, to mitigate climate change. As the world seeks cleaner alternatives, hydrogen has emerged as a promising solu- tion. TotalEnergies, a prominent energy company, has invested substantially in hydrogen technology to facilitate the transition toward sustainability. Although commendable, it is essential to recognize that the current environment remains a high-risk prototype phase. The successful establishment of hydrogen infrastructure and mobility services demands meticulous planning, robust technological advancements, and comprehensive risk assessment.
Thus, there is a need to comprehend the potential areas of development within a high-risk prototype environment by harnessing the company’s extensive 40 years of experience in Reliability, Availability, Maintainability, and Safety (RAMS) analysis. This need entails conducting a comprehensive and systematic RAMS analysis specifically tailored to the unique challenges and requirements of hydrogen refueling stations. However, a notable gap exists in the existing literature, as there is a lack of established guidelines or published research that specifically addresses the intricacies of RAMS analysis for the stations. This knowledge gap poses a significant burden in effectively assessing and mitigating risks, ensuring optimal performance, and fostering the safe and reliable operation of hydrogen refueling infrastructure. In order to address this critical knowledge gap, the main research question has been formulated as follows:
Research Question – Main: How can a systematic RAMS study-based approach be developed to improve the availability of hydrogen refueling stations by leveraging conventional RAMS methodological frameworks?
An extensive literature study was conducted to address the main research question. The aim of that part was to gather insights and identify best practices from existing research from other industries. As a result of the literature study, a Systematic Techno-Economic RAMS Analysis (STRAMSA) framework was developed. The STRAMSA framework introduces significant contributions to RAMS analysis for hydrogen refueling stations. It integrates system engineering, RAMS analysis, and techno-economic analysis to maximize the availability of system design. The framework establishes a strong link between RAMS analysis and techno-economic evaluation, facilitating informed decision-making by considering financial aspects. In techno economic analysis, it incorporates novel elements such as inflation, learning-by doing effect (in terms of market growth), and deflation rates for hydrogen. Moreover, the framework separates maintenance costs from operational costs to facilitate targeted improvements for the operation. This part also quantifies safety by assessing risks in monetary terms for accidents/fatality occurrence. Lastly, its iterative nature allows continuous improvement and adjustment throughout the design and operation process. Overall, the STRAMSA framework provides a comprehensive approach for analyzing the reliability, availability, maintainability, and safety aspects of systems.
To evaluate the applicability and effectiveness of the STRAMSA framework, the Total Energies Breda Hydrogen Refueling Station was selected as a case study. The steps outlined in the framework were applied to this specific station. Throughout the process, an iterative approach was adopted, allowing for adjustments to the framework as necessary.
After applying the STRAMSA framework to the Breda Hydrogen Refueling Station, the Net Present Value (NPV) was calculated as 1,815,202.69 €. The analysis also revealed the uncertainty of attaining a positive NPV, with a 11.18%. Further investigation into the sources of uncertainty identified market-related parameters as the dominant contributors to the variance. Specifically, parameters such as ”Market Growth Rate,” ”Hydrogen Price,” and ”Hydrogen Sale” significantly influenced the uncertainty surrounding the NPV. These findings underscore the importance of a coordinated policy approach that encourages investments in both the hydrogen market and hydrogen infrastructure. Such an approach is crucial for the rapid adoption of hydrogen technology and the development of a robust hydrogen economy. In addition to market parameters, from the operational perspective, the dispenser is identified as a decisive contributor to the uncertainty that requires closer examination.
As a further theoretical research, exploration the applicability of the STRAMSA framework for multi-case studies can be conducted. Initially, the framework can be applied to hydrogen refueling stations as a potential case study, but its feasibility can also be assessed for other industries. Furthermore, the impact of a design configuration change can be evaluated for the case at stake. One potential modification to consider is the addition of a supplementary High-Pressure (HP) compressor, as this particular component has been identified as the least reliable subsystem based on the RAMS analysis. Conducting such assessments can provide valuable insights into the effectiveness and adaptability of the STRAMSA framework in different scenarios and improve the design of hydrogen refueling stations.

To combat the effects of climate change, there is a worldwide shift to reduced emissions and increased use of renewable energy sources. Solar energy is a vital part of this transition and necessary to be able to achieve the targets. The rapid pace of adaptation raises questions about the negative equity and justice associated with this development. Along with the rapid growth, regional differences in solar panel adoption have been found across the world. Besides, government incentives and policies have played an important role in both adoption growth and justice implications.

This study explores the pace and equitability of the solar energy transition in Amsterdam, the Netherlands. The study evaluates the potential for solar energy, the disparity of adoption, what socio-economic factors are correlated to adoption patterns, and how adoption might evolve in the future and under different policy measures. To do so, a structured, integrated and data-driven approach is designed to meet the research objectives, and possibly serve as a policy tool for future studies.

The study shows that there is an "adoption gap" in Amsterdam, meaning that solar panels and the benefits of incentives end up with a specific group of citizens. Therefore, targeted policy measures are necessary to ensure an equitable transition.

This research uses high resolution satellite images in combination with an unsupervised Convolutional Neural Network Autoencoder to identify features that can be used to cluster different built environment typologies. Previous remote sensing research uses ground truth data which for some areas is not available or needs manually labeled training data. This research attempts to circumvent the issue of information scarcity in order to create a methodology that can be applied on any city as long as satellite images are available. From the resulting clusters, clusters can be selected which represent areas with high levels of deprivation which in turn can help identifying the deprived areas.

Over recent decades there have been advances in the research behind adaptive policy approaches. More recently, emerging qualitative approaches for managing deep uncertainty has drawn the attention of planners in the water industry, particularly in water utilities. Despite interest from both the water industry and the research community to see these novel approaches applied, there have been limited applications and no published guidance to support the operationalisation of these approaches in water utilities. This thesis seeks to bridge this gap by answering the question: “What are the factors influencing the adoption of adaptive policy approaches by water utilities?” To answer this question, a design science approach was used to understand current barriers and enablers to the adoption of such methods in water utilities, and to design a framework to support adoption of adaptive approaches. This work was conducted through a grounded theory analysis of interviews of relevant water utility practitioners in Australia and the Netherlands and members of the decision making under deep uncertainty and adaptive planning research community internationally. The outcome of this study is a maturity framework of barriers and enablers to the uptake of adaptive approaches in water utilities, designed to support utilities and researchers in evaluating their level of adoption and to identify strategies for increasing the implementation of adaptive approaches where appropriate.

More and more people are living in cities. As these cities grow, the benefits of living in them are increasingly unequally distributed. One factor that can turn the tide of glooming urban inequality is equitable access to opportunities. Designing policies for equitable access requires taking into account several components that impact accessibility: (i) land-use, (ii) transportation, (iii) individual needs and opportunities, and (iv) temporal constraints. In this research, the interaction between a novel mode of public transport, microtransit, and the first three components of accessibility is investigated. The accessibility implications of microtransit in the case study area: the Southern suburbs of the Minneapolis-St. Paul metropolitan, are assessed. It is concluded that microtransit manages to reach vulnerable rider groups and interacts with the land-use environment by pairing residential areas with commercial centers. Microtransit also interacts with other modes of transit by adding both extra demand and lowering the demand for ride-hailing and personal car use predominantly. Microtransit rarely seems to replace public transit or walking alternatives. Overall, it is found that microtransit does significantly increase accessibility levels in the case study area and especially impacts groups with low car ownership. Lastly, the benefits of microtransit are more fairly distributed than those of traditional transit in the studied area. This research demonstrates the positive access equity impact of microtransit and suggests further research into choice-riders, the application of microtransit in urban (core) and rural environments, and the interplay between public transit and microtransit, as well as policy interventions for policy-makers and transit providers.

The Akosombo dam in Ghana's Lower Volta River Basin provides essential economic benefits through hydropower generation, flood protection, and irrigation opportunities. However, since its construction was completed in 1965, the livelihood of the riverine communities drastically changed. The seasonal peak and low flows in the river, which provided environmental services for many downstream households, are now replaced with a stable river flow throughout the year to favour hydropower production. A study on dam reoperation is not only of interest to look into the possibilities of restoring healthy ecosystems but also to look at how current water demands in the basin can be sustained. Climate change and increased water demands for irrigation are expected to strain the water resources in the Lower Volta. Reoperation is also complicated by the absence of water treaties with neighbouring countries. This can result in lower flows from upstream riparian states due to their increasing water needs. In this study, we aim to understand the vulnerabilities of the Akosombo system while finding robust policies that can cope with challenging climate and demographic conditions. This study highlights the challenges in balancing irrigation, environmental, and hydropower goals while also identifying system vulnerabilities and opportunities for robust operations to meet the Lower Volta River water demands under future challenging conditions. This is done by reevaluating previously found optimal release policies under uncertainty. From this study, it can be concluded that the uncertainties included in the analysis will alter system functioning. Not only will floods occur with the onset of climate change, but energy production and irrigation potential will also become lower if upstream states start to use more water. The most critical abstractions are those of Togo and Côte d'Ivoire. A benefit of this is that these abstractions also provide some protection against floods. It is therefore wise to create cooperation projects with these countries to safeguard the current benefits of the Akosombo and Kpong dam and create new benefits for all parties.

The cities of the Global South need to become more flood resilient in light of climate change and rapid urbanisation. In this thesis, a land-use modelling case study for the Mumbai Metropolitan Region is executed on the interaction of flood risk and land use, through which future flood resilience was assessed for several urban planning strategies. Concluded was that the urban environment will become less resilient in case no governmental intervention occurs, and urban planning can contribute to the resilience of the urban environment. Furthermore, the effectiveness of urban planning was largely dependent on the strictness with which these policies were implemented and regulated, but largely independent of the perceived climate future.

Leakage is the main source of water loss in water distribution networks (WDNs). Therefore, leak detection and localization technology is a major concern for water utilities to save water and meet the ever-growing water demand. This study presents two methodologies for leak localization in District Metered Areas (DMAs): (1) a model-based method using the hydraulic model, flow and pressure measurements, as well as leak flow; (2) a data-driven method that relies on graph-based interpolation. The performance of the model-based method is proved to be negatively affected by model errors and limited sensors. To solve these two problems, on the one hand, flows and residuals between observed and model-simulated data in the non-leak situation are used to develop a residual model to calculate offset values for model output correction. On the other hand, graph-based interpolation is introduced to create ‘virtual’ sensor measurements in the presence of a limited number of sensors. The data-driven method proposed in this work uses graph-based interpolation to estimate the head signals at the nodes without sensors and subsequently create pressure maps. Leak localization is achieved by comparing pressure maps in the non-leak and leak situations. In this process, this methodology does not require a well-calibrated model and leak flow information. Both two methodologies are tested on fire hydrant leak tests in DMAs in the United Kingdom. Results obtained by using the model-based method illustrate the positive impact of model output correction on localization results and the performance of this method under different conditions such as different times of the leak and different sizes of leak flow. The data-driven method performs fairly well in DMAs with a higher spatial density of sensors. Furthermore, the results of both two methods are compared to demonstrate the suitability of the methods in different cases.

It is vital for adequate management, and operation of water distribution systems (WDS) to have reliable short-term water demand forecasts. Conventional time-series models present limitations when dealing with non-linear changes in water demand. Thus, it is proposed to employ deep learning algorithms to offer a more reliable forecast. Three models are used, two 1-dimensional convolutional neural networks (1D-CNN), (a simple CNN, and a dilated causal CNN), and a recurrent neural network, particularly a long short-term memory (LSTM) based model. The performance of the models is tested on seven real-life water distribution systems in Italy with different uses and number of users. Also, a comparison with benchmark algorithms based on time-window techniques and pattern-based models is made. Additionally, the use of meteorological variables such as rainfall occurrence, temperature, and relative humidity is intended to test whether there is a positive effect on the forecast. Furthermore, a global model is built taking several years of data for training to test whether this bigger model increases generalization and improves accuracy in comparison to the individual cases. In addition, transfer learning is employed to predict individual cases and a WDS in the Netherlands. Lastly, a bigger global model is built and trained with 14 years of data to improve the performance of transfer learning on the Dutch WDS. To begin with, it was seen that 1D-CNNs outperformed the LSTM-based model, and the benchmark algorithms using data of the water demand, and a binary index indicating whether it is a weekday or a weekend day for six of the seven case studies. For the remaining case study, the results indicated that there is less than 1% in error between the best benchmark model and the proposed 1D-CNN algorithms. Moreover, the addition of meteorological variables showed to improve the calibration performance of the models but worsened the predictions on unseen data. It was observed that a simple 1D-CNN overfits when adding these extra variables due to its lack of regularization. Also, the global model showed to improve in accuracy compared to the individual models. The use of transfer learning (TL) did not indicate to improve the performance of one of the case studies, nonetheless, TL showed that by only using 75% of the data for training, the model offers a good generalization on the case with sudden changes in demands by the rapid increase of users due to seasonal touristic activities. For the Dutch WDS, TL performed similarly to the individual model, there errors ranged between 15% and 16% using different quantities of data for training. In addition, when having no data for training, the pre-trained model displayed showed lower than using 25% of data for training for the Italian cases. Lastly, the bigger global model performed in the same way as the smaller global model on the Dutch WDS. Also, when having no data for training, the model performed better.

Urban areas are seeing influx of population and therefore are experiencing increasing stress on the systems currently in place. In traffic networks, a larger population means that their are inevitably a short term increase in mobility demands. To accommodate this, governments and and the private sector are proposing new solutions for mobility. One such focus is the introduction of new transport modes. A common classification for these modes is Mobility-as-a-Service (MaaS), which includes modes such as carsharing, shared last-mile transportation like bicycles, and ridesharing. MaaS also overlaps with other new modes such as autonomous and electric vehicles. Modeling these types of modes and their subsequent interactions with each other and traditional modes requires more complex modeling techniques than have been traditionally used in transport modeling.

The purpose of this thesis is to propose a method for using agent-based modeling and simulation (ABMS) to assess traffic network resilience, as ABMS has the dynamic qualities that match well with the time variant nature resilience.. This gap is especially prevalent when considering novel modes of transport, such as ridesharing. Data from the metropolitan region of Rotterdam and The Hague (Metropoolregio Rotterdam Den Haag or MRDH) is used for a case study on the corridor between Rotterdam and The Hague. The goal is to prove feasibility of a method for assessing resilience in traffic networks using ABMS.

A resilience framework for urban mobility is proposed that uses six categories to characterize resilience: (1) reflective, redundant, flexible, resourceful, inclusive, and integrated. Using this framework as a guide, three metrics are proposed for measuring resilience using agent-based simulation: origin-destination (OD) travel time, link travel time, and link volume. These metrics are used to compare scenarios that either include a disturbance in the network or do not, which in this case occurs for a 30 minute period on the A4 roadway between Rotterdam and The Hague. While the OD travel time metric is limited in its usability, the link travel time metric makes apparent the the recovery time to achieve normal operating conditions after a disturbance. In the presented case study, scenarios that included ridesharing had worse recovery time then the car only scenario, as well as a higher maximum travel time across the disturbed link. The link volume metric contextualizes these results, showing that while the overall volume throughout the simulation is lower for the ridesharing cases, the volume during the disturbance across the disrupted portion of the A4 roadway is higher. The higher volume shows why the travel time is higher with the presence of ridesharing in the disturbance scenario. These results are subject to the limitations of the model, though, which include dynamic routing that may not avoid the disturbed portion of the network when the disturbance occurs.

Due to climate change, extreme phenomena like droughts are going to be intensified. Even though droughts in agriculture have been studied, regarding urban environment their consequences are rather unexplored. Cities are susceptible to droughts and the estimation of their vulnerability is the first step for their protection. The objective of this thesis is to determine vulnerability of cities to groundwater and soil moisture droughts. Since drought is a complex phenomenon which is difficult to define, its analysis is not straightforward especially in cities. For that reason, an urban drought categorization framework is created. In the current research, two of the four drought categories were studied (groundwater and soil moisture droughts). A case study for the vulnerability calculation was chosen for Leiden, the Netherlands. Before determining its vulnerability, different techniques to identify drought exposure characteristics were investigated. More specifically, regarding deficit and duration, the following methods were used: (i) fixed, (ii) variable, (iii) moving window method, and (iv) median groundwater level as threshold for the case of groundwater droughts. Spatial analysis was performed to estimate the areal extent of droughts whereas frequency distribution analysis is assessed of the minima of monthly blocks. A similar approach was applied for soil moisture droughts. Soil moisture is modeled via the lumped Urban Water Balance Model. Vulnerability was estimated as the aggregation of exposure and sensitivity (physical and social). For both components of vulnerability, their indicators were normalized. Out of the four drought characteristics (deficit, duration, spatial extent, and frequency), only the two first were included as exposure indicators for the vulnerability estimation. The indicators' weights were computed using the Analytical Hierarchy Process (AHP). Vulnerability estimation was applied for both groundwater and soil moisture droughts separately. One of the main results is that variable threshold performance is higher than fixed threshold and moving window for deficit and duration estimation regarding groundwater droughts. However, a combination of fixed and variable threshold can provide a profound insight into drought exposure. That applies to soil moisture droughts too. To increase performance of deficit and duration identification, pooling can be applied. An inter-event time of around 10 days for the case of variable threshold and 30th percentile is suggested regarding groundwater droughts. The analysis on soil moisture droughts was not sufficient to draw conclusions regarding inter-event time. Vulnerability follows sensitivity patterns - rather than exposure pattern - for both groundwater and soil moisture droughts at both fine and coarse space resolution. Besides, out of all indicators used, those which contribute to vulnerability variation the most were determined for both studied types of droughts. Those indicators are: `land use' and `percentage of households belonging to the lowest 40% income nationwide' for groundwater droughts whereas for soil moisture ones, it is `green areas'. Another result is that the differences in vulnerability vary marginally using different techniques to identify drought events for both studied types of droughts. Therefore, even applying a sophisticated technique to identify drought events, will not lead to significant improvement regarding vulnerability estimation. All aforementioned conclusions regarding vulnerability are highly uncertain since drought experts assigned different indicators' weights; the convergence of opinions was low. Based on the proposed methodology, water managers would be able to determine vulnerability of cities to droughts and policy makers would be able to protect the regions which are highly vulnerable. Consequently, the adverse impacts of droughts on cities could be mitigated, reducing residents' hardship.