sobakrim/Two-stage-CNN-LSTM
Hybrid CNN-LSTM for learning the spatio-temporal relationship between wind and significant wave height
This project helps oceanographers, coastal engineers, and maritime safety professionals predict significant wave height (Hs) from historical wind field data. You input a sequence of wind field snapshots, and it outputs the corresponding time series of significant wave height. This is useful for understanding past sea conditions and can aid in coastal planning and risk assessment.
No commits in the last 6 months.
Use this if you need to analyze and predict significant wave heights based on past wind patterns, particularly for hindcasting sea states.
Not ideal if you need real-time operational forecasting or if you lack historical wind field data.
Stars
18
Forks
4
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jun 27, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sobakrim/Two-stage-CNN-LSTM"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
NVIDIA/earth2studio
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
mllam/neural-lam
Research Software for Neural Weather Prediction for Limited Area Modeling
chengtan9907/OpenSTL
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
NVIDIA/earth2mip
Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate...
aditya-grover/climate-learn
Source code for ClimateLearn