Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior for sustainable decision-making. Currently, these insights are gathered by process-driven models that are often time expensive, require local input and calibration, and are often limited by equations. However, satellite imagery proved to be a promising data source to derive historic shoreline behavior on a global scale. Machine Learning (ML) algorithms are suggested to create an extra in-depth understanding of these satellite images. The increase in the availability of both satellite data and ML algorithms opens possibilities for shoreline forecasting on a global scale. This research aims to improve forecasting of shorelines by creating a global model in which cross-time series information can be used. To encourage a meaningful exchange of time series information, a clustering forecasting approach is proposed.This research builds upon the yearly SDS data behind the Shoreline Monitor of Deltares. The SDS dataset captures shoreline evolution and behavior on a global scale by quantifying it in time series. The SDS dataset consists of transects every 500 m along the global coast and contains 33 years (1984-2016) of shoreline evolution. First, in order to cluster transects, the features for clustering were defined. Here, clustering features were divided into time series features and hydraulic and geomorphic features. Whether transect clustering could improve shoreline forecasting, based on these time series and hydraulic and geomorphic features, was explored. For this thesis, it was decided to focus on time series features for further clustering usage. Eventually, a shape-based transect representation was chosen as feature for input of the clustering algorithm. Around 350,000 globally distributed sandy transects were assigned to nine different main clusters, with a semi-unsupervised clustering approach. These nine clusters captured global sandy shoreline behavior ranging from extreme erosion to extreme accretion and proved to be a practical tool to quantify the (distribution of) shoreline behavior on different spatial scales. Subsequently, a second supervised step was developed to create sub-clusters to gain more insights into the trends of the nine clusters. With this supervised sub-clustering step, three sub-clusters were generated for each of the five largest clusters, allowing for the distinction between accelerating and decelerating behavior in the last decade of time series per cluster. Global, country-level and local case studies showed the potential of these sub-cluster refinements. Hereafter, the nine main clusters were separately used as input for four different forecasting algorithms. This resulted in predictions per cluster with a more reliable and more accurate seven-year forecast for >95 % of the transects, sometimes improving accuracy up to factor 15, compared to recently published work. Furthermore, a multi-method approach was created to determine the most reliable forecast, on a local scale by combining overall accuracy and information gained during clustering. Time series clustering enhances the forecasting of transects based on overall accuracy and local reliability. The multi-method approach for selecting the appropriate prediction on a local level, could be a practical tool for governments to apply. Besides, a vulnerability assessment explores the possible application of the predictions and underlines the need for global databases regarding the coastal zone. However, further research should consider incorporating hydraulic and geomorphic features to strengthen the clustering of transects. Hence, shoreline forecasting based on historical data should be considered as a valuable tool for sustainable decision-making regarding coastal zones.

The goal of this study is to examine the feasibility of this solution, from a water quality and hydraulic point of view. Firstly, the current state of the three water bodies was investigated and a stakeholder analysis was conducted to look into the social and political context. Secondly, the effect of the solution on the water quality in the WL and TLR was researched. The most important water quality parameters were qualitatively discussed and after that, the quantitatively changes in the WL were modelled. A convection-diffusion model was set up for different parameter concentrations in R. The initial parameter concentrations were gathered by field measurements and extensive online research. The water quality assessment shows that the Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), turbidity and Total Suspended Solids (TSS) concentrations in the RR have a better value than in the WL. The model itself shows for every parameter that after 100 days of mixing the water is not completely mixed in the WL. The water in the TLR is flushed with WL water and its quality is therefore more or less equal to WL water. It is concluded that the proposed solution improves the water quality in the WL and the TLR. However, long and frequent mixing is necessary for the WL water to reach the RR water quality level. Thirdly, a hydraulic analysis was carried out by investigating the hydrological and geometrical characteristics of the three water bodies. Thereafter, the hydraulic impact was examined by comparing six different flushing scenarios.The flow, water depth and sediment transport rates for different time intervals were modelled in R over the distance of the TLR. A Multi Criteria Analysis was done to interpret the results on the consequences in the TLR. The outcome of the optimum scenario is when the gate has an opening height of 5%. The total transported sediment volume is significantly larger in this scenario, which is beneficial. From a hydraulic perspective the proposed solution is feasible for all water bodies. However, more extensive research on for example the impact on the hydraulics in the TLR is needed to get more conclusive results. As a spin-off, our project (co-)developed educational tools that can be used within the HUNRE curriculum and as awareness raising activities in Hanoi with citizens, schools and the like. Fieldwork with Vietnamese students was conducted to start with building a data base on water quality of the water bodies in Hanoi. The tools that are created are manuals, instruction videos and an introduction lecture. Furthermore, the database, that is built, can be extended with more fieldwork in the future. The extent to which the educational tools and the database integrate in the study program of HUNRE remains unsure, however the awareness among Vietnamese students on the importance of water quality has increased. Furthermore, the OKP project strives to realize the integration of the educational tools in the future. Therefore, this research objective is expected to be achieved.