Dynamic Adaptive Policy Pathways for flood risk management in Galveston Bay: Making informed flood defence decisions for an uncertain future

Making decisions when future conditions are uncertain is a challenging endeavor. This thesis develops a framework to analyse flood risk and create Dynamic Adaptive Policy Pathways, which can provide insights in the behaviour of flood risk protection measures in many future scenarios. The pathways are used to identify robust measures, dead ends...

Advancing Flood Risk Screening

Manycoastal cities are struggling with a rapidly growing risk of flooding. The sizeand complexity of these cities often demand a coordinated strategy, consistingof a combination of flood risk reduction measures. A crucial part in the designprocess is the identification of effective flood risk management strategies. However,data and resources are...

Belief-Informed Robust Decision Making (BIRDM): Assessing changes in decision robustness due to changing distributions of deep uncertainties

Robust Decision Making (RDM) is an established framework for decision making under deep uncertainty. RDM relies on the idea of scenario neutrality, namely that decision robustness is not affected by how scenarios are generated if these are uniformly distributed and span a sufficiently large range of future states of the world. Several authors...

System-based replacement of hydraulic structures: A study to replace the weirs in the river Meuse

For centuries humanity has been engineering the second largest river system in the Netherlands, the river Meuse system. This resulted in improvements for the shipping and water safety functions, but simultaneously has had a negative impact on other functions of the river Meuse system. Nature and water quality, for example, degraded due to the...

Taking deep uncertainty into account in traffic models: A case study of Groningen

Covid-19 has proven how uncertain the future can be. However, traffic models fail to fully acknowledge this uncertainty. Therefore, decisions made based on these models are not very robust. In this research, Robust Decision Making (RDM) method is applied on a macro-level traffic model made for the municipality of Groningen. This method,...

Towards a zero-emission naphtha cracking industry in the Netherlands: A model-based exploration of policy options to accelerate electrification

Following the Paris Agreement, the national government of the Netherlands reached the Climate Agreement together with more than 100 organizations. As part of this agreement, the industry sector is faced with the ambitious goal of 59% CO2 reduction in 2030 with respect to 1990 levels. Since the naphtha cracking industry is one of the most...

Assessing Alternative Fuel Types for ULCVs in Face of Uncertainty

The International Maritime Organization forecasts that greenhouse gasses produced by ships will increase rapidly in the near future, unless precautions are taken now. This will require all ships to reduce their emissions, especially container vessels which are producing the most emissions. One way of reducing emissions, with existing technology,...

Evaluating Multi-Disease Interventions

Policymakers who work in the public health sector may rely on the help of quantitative models to support their choice of control strategy against a particular infectious disease. While policymakers have a large number of decision support models to choose from, hardly any of these tools are used to design an intervention strategy that can work...

Long-Term Adaptive Flood Risk Management: Investing in coastal protection and stormwater management under deep uncertainty

Flood risk management is the process of analysing, assessing and (if required) reducing flood risks, in terms of economic costs and affected population or loss of life. The analysis is performed through a probabilistic approach in which the factors are represented by a probability distribution function to address uncertainties. However, relevant...

Robust Decision Support Methods: A Comparative Analysis

Methods for decision support in the context of deep uncertainty have been gaining interest in the context of complex and adaptive problems that are characterized by “tipping point” behaviours. Unlike a traditional “predict-then-act” methods, which determine policies based on specific predictions of future behaviour, these decision support...

Including robustness considerations in the search phase of Many-Objective Robust Decision Making

Many-Objective Robust Decision Making (MORDM) is a prominent model-based approach for dealing with deep uncertainty. MORDM has four phases: a systems analytical problem formulation, a search phase to generate candidate solutions, a trade-off analysis where different strategies are compared across many objectives, and a scenario discovery...

Improving scenario discovery by bagging random boxes

Scenario discovery is a model-based approach to scenario development under deep uncertainty. Scenario discovery relies on the use of statistical machine learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method (PRIM). This algorithm identifies regions in an uncertain model input space that are highly...

Exploratory modeling: A tool for communicating the future: A case study of valuing flexibility in flood defense strategies

Decision-making for flood defense while facing a considerable change in driving forces demands other methods than the traditional approach of forecasting and optimal policy selection. Exploratory modeling can be a candidate for helping adaptive policymaking to deal with the uncertainties that confront decision-makers. In adaptive policymaking...