Deep Learning-Driven Wave Height Prediction: Exploration of RNN and CNN Applications in Marine Engineering

This project focuses on using deep learning technologies (RNN and CNN) to model and predict time-series wave data from wind tunnel experiments, aiming to provide data support for fields such as marine engineering and shipping safety. By comparing the performance of the two neural network architectures, it explores the possibility of breaking through the limitations of traditional wave prediction methods.

Wave height prediction is crucial for fields like marine engineering, shipping safety, and coastal protection. Traditional methods rely on physical models and numerical simulations but struggle to capture the nonlinear characteristics and long-term dependencies of complex marine environments. Deep learning has significant potential in time-series prediction: RNN excels at handling temporal dependencies, while CNN can extract local features. Their combination is expected to break through traditional limitations.

The ECS_RIDE_Wave_Height_Prediction project was developed by AarejSyed using the TensorFlow/Keras framework to explore the performance of RNN and CNN in wave prediction. The data comes from time-series wave data generated by wind tunnel experiments, which features high sampling frequency, controllable variables, and complete annotations, simulating the wave generation process in real marine environments.

RNN Approach: Uses LSTM (to solve gradient vanishing and learn long-term dependencies) and GRU (a simplified version with fewer parameters) to capture the temporal dynamic patterns of wave heights. CNN Approach: Extracts local features (such as wave crests and troughs) via 1D convolution, with high parallel computing efficiency and parameter sharing to reduce overfitting. The project also suggests the possible use of a CNN-LSTM hybrid architecture, combining the advantages of feature extraction and temporal modeling.

Implementation Details: The model is built based on TensorFlow/Keras and supports GPU acceleration; a command-line interface (cli.py) is provided to facilitate hyperparameter configuration and experiment reproduction. Application Scenarios:

1. Maritime Shipping Safety: Assists in route planning to avoid dangerous sea conditions;
2. Coastal Engineering Design: Optimizes port and breakwater design;
3. Offshore Wind Power Operation and Maintenance: Optimizes wind turbine layout and maintenance plans;
4. Marine Science Research: Helps understand wave generation mechanisms.

Challenges:

1. Data Generalization: There are differences between wind tunnel data and real marine environments;
2. Extreme Event Prediction: Data on extreme sea conditions like typhoons is scarce;
3. Physical Consistency: Pure data-driven methods may violate physical laws. Future Directions:

• PINN (Physics-Informed Neural Networks) combined with physical equations;
• Multi-source data fusion (satellite, buoy, numerical simulation);
• Uncertainty quantification to provide prediction confidence intervals.

This project is an application exploration of deep learning in the marine engineering field, providing technical references for wave height prediction by comparing RNN and CNN architectures. Although its scale is compact, its methodology and implementation ideas are of reference value to relevant researchers. With technological development and data accumulation, data-driven wave prediction is expected to achieve greater breakthroughs in accuracy and practicality.