Neural networks for spatial and temporal ocean wave height prediction considering coastal morphodynamics in the East Frisian North Sea / vorgelegt von Christoph Jörges. Wuppertal, Dezember 2022
Content
- List of Figures
- List of Tables
- Nomenclature
- Abstract
- Kurzfassung
- Introduction
- Introduction and motivation
- Emerging climate-related hazards to the North Sea coast
- State of the art and basic related work
- What waves are and how they are measured and modeled
- Neural networks and their application for wave height prediction
- Study area and ETD sandbanks
- Research questions, aims, and objectives
- Research Study I
- Introduction
- Research area and data
- Morphodynamic analysis of the ETD sandbanks by GIS
- Analysis methods of ETD sea state damping
- Buoy data
- Statistical methods of natural sea state variability
- Numerical modeling
- Model validation and transfer to test cases
- Sea state damping effect
- Results and discussion of ETD sea state damping
- Natural wave climate variability
- Sea state damping effect – impact of ETD sandbanks on sea state
- General findings of the buoy-measured damping effect
- Conclusions
- Research Study II
- Introduction
- Methods
- Research area and dataset
- Morphodynamic influences and bathymetry preprocessing
- Long Short-Term Memory neural networks
- Model settings
- Loss function, optimizer, and error metrics
- Hyperparameter tuning
- Results and discussion
- Model comparison – nearshore excluding bathymetry features
- Model comparison – validation on offshore data
- Impact of bathymetry features
- Reconstruction including bathymetry
- Prediction including bathymetry
- Limitations of including bathymetric data
- Further feature selection analysis
- Conclusions
- Research Study III
- Introduction
- Material and methods
- Study area
- Metocean data
- ETD sandbank characterization and geostatistical bathymetry simulation
- SWAN modeling
- Neural network data preprocessing
- Convolutional neural networks (CNN)
- Neural network model settings and architecture
- Loss function, optimizer, and error metrics
- Hyperparameter tuning
- Results and discussion
- Model performance of spatial prediction
- CNN neural networks vs. SWAN numerical model
- High-resolution uncertainty estimation on the coastline
- Application of the proposed method for a climate change scenario
- Conclusions
- Synopsis
- Discussion
- Coastal morphodynamics, ETD variability, and damping effect
- Bathymetric data and observation methods for improving predictive sea state modeling
- ML vs. numerical modeling – Spatial wave predictions using synthetic and real bathymetry data
- Conclusions
- Outlook
- Summary
- Bibliography
- Appendix