Today's coastal zones are densely inhabited as the majority of the world's population lives in these attractive areas. The shorelines in coastal zones are shaped by complex spatial and temporal variable interactions between natural forcings like changes in mean sea-level, tides, wave and wind conditions, and storm surges. Besides, natural hazards such as coastal erosion, tropical cyclones, hurricanes, typhoons, floods, salt intrusion and tidal surges threaten a major part of the world's population. Furthermore, climate change is likely to increase the risk of natural hazards. As a response, humans changed the world's shorelines and the forcing-driven processes that work on them, to increase protection against hazards and keep supporting their activities. These human interventions are deployed discontinued in time, disperse spatially and might result in negative consequences leading to human-induced hazards. This research focuses on one particular hazard to coastal communities, coastal erosion, which is extended to a term referred to as shoreline evolution by incorporating coastal accretion as well. Up till now, detailed local-scale studies are able to expose human and natural drivers of shoreline evolution and provide a possibility to make a step towards intentional rather than accidental coastal engineering. Digital imaging and, more recently introduced, satellite imagery proved to be a promising new technology to measure and monitor shoreline evolution at bigger temporal and spatial scales. Nevertheless, the opportunity to develop a model that exposes the drivers of shoreline evolution on a planetary scale remains unexplored. This is due to the required computational effort as well as the large variability in coastal systems around the world. With an ever-increasing data availability, data-driven models incorporating Machine Learning (ML) proved to be an efficient alternative approach to heavy computing classical process-driven models in civil engineering practice. Next to this, a coastal classification can be used as a means to inventory the aforementioned variability. Therefore, the research objective in this study is to explore the possibility of exposing and classifying the drivers of shoreline evolution on a planetary scale, by employing ML on satellite imagery. Approximately 390.000 km of shoreline is analyzed for the past 33 years. This resulted in a statistically derived classification of natural and human-induced sandy shoreline evolution. By elaborating on this classification, it is found that natural and human-induced shoreline evolution accounts for approximately 16 and 25% of the total of globally exposed and classified shoreline evolution signals respectively. All outcomes in this research can support detailed local scale investigations and therefore provide an enhanced opportunity to make a step towards intentional rather than accidental coastal engineering. Hence, it is concluded that the developed and applied methods that employ ML on satellite imagery can be used to expose and classify (in)direct human and natural influences on sandy shoreline evolution using spatial and temporal characteristics on a planetary scale. 57% of the shoreline evolution signals is present in a regime with complex and combined (compound) influences, which still requires (rather than supports) local scale investigations to determine the correct influence or driver. More research is required to elaborate on the opportunities that can enhance insight in the compound regime, to improve the obtained results and advance the applicability of this study.

The Clarence River catchment is located in the state of New South Wales (NSW), on the east coast of Australia. The lower Clarence Valley is an area covering approximately 1000 square kilometers and is located on the downstream part of the Clarence River. Due to heavy rainfall, the Clarence River discharge can increase from an average 160 m3/s to 20000 m3/s. As a result, water levels rise significantly leading to severe floods in the Clarence Valley. The main urban areas in this region, Grafton, South Grafton and Maclean, are located in narrowing river bends which makes them particulary vulnerable to flooding during high water levels. The main goal of this report is to present flood mitigation measures to reduce the impact of flooding in the urban areas of the Clarence Valley, based on the Duthc flood mitigation strategy called 'Room for the River'. Consequently the following research question was formulated: How can the impact of flooding on the urban areas in the Clarence Valley be reduced by increasing the storage capacity of floodplains? In order to answer the research question, the following project approach is applied. Six areas were identified, based on a fieldvisit and an extensive preliminary study, to implement flood mitigation measures and assess existing flood defences. Part of these flood defences are the Swan Creek Floodgate and the reinforced concrete levee wall of Maclean, which will be investigated on their performance. A fully calibrated numerical floodmodel provides input for the hydrological analysis. The model represents the current situation in the Valley. Scenarios are created by applying topographic adjustments. The new scenarios are implemented into the numerical model and the effectiveness on flood mitigation in urban areas is assessed by comparing the results of a 5, 20 and 50 year Average Reccurance Interval flood event to the current situation during one of these flood events. By making use of the proposed floodplains and improving the performance of existing flood defences, the flood defence system of the Clarence Valley can be extended. It can be concluded that it is possible to reduce the impact of flooding in the urban areas of the Clarence Valley by increasing the storage capacity of floodplains around Grafton. Therefore, the usage of a ’Room for the River’ strategy can be a solution to the problems the Clarence Valley is facing, and possibly might be applicable to more flooding-vulnerable areas in Australia.