Short description and objective of the project:
Help develop a data-informed method for predicting shoreline evolution and informing nourishment design and coastal management decisions. As a graduation intern at Haskoning, you will combine satellite observations, shoreline modelling and machine learning in a workflow with direct engineering relevance. Coastal engineers increasingly use satellite-derived shoreline observations to analyse shoreline behaviour over large spatial and temporal scales. More specifically:
· CoastSat: an open-source toolkit that extracts shoreline positions from satellite imagery and has become widely used for coastal monitoring and research.
· ShorelineS: a physics-based coastline evolution model developed by Deltares that simulates shoreline change under varying wave and sediment transport conditions.
This thesis investigates how satellite-derived shoreline observations from CoastSat can be used to automatically calibrate ShorelineS. The objective is to develop a probabilistic, data-informed calibration workflow inspired by approaches such as CoSMoS-COAST, while extending the methodology to more complex environments with structures, nourishments, and other forms of human interference. To achieve this goal, you will:
· Set up a ShorelineS model using nearshore wave conditions and initial coastline geometry.
· Design and evaluate an automated calibration framework for ShorelineS using satellite-derived shoreline observations.
· Evaluate parameter uncertainty, model skill and the implications for complex coastal areas with human interference such as structures or nourishments.
Head Graduation Committee:
To be determined (dr.ir. Jebbe J. van der Werf, dr.ir Pieter C. Roos or other)
Daily Supervisors:
Arend Timmer (Haskoning), supplemented with supervisor from UT t.b.d.
Are you interested in this assignment? Contact the Master thesis coordinator:
