Unfortunately using this model is rather complicated and involves a large number of steps. There is no way around this. This README (will) explain it the best I can though.
The research in this repository has been published in a conference paper(!)
- **Title:** Towards AI for approximating hydrodynamic simulations as a 2D segmentation task
- **Conference:** Northern Lights Deep Learning Conference 2024
- **DOI:** coming soon, but in advance you can view what should be the final paper here: <https://openreview.net/pdf?id=TpOsdB4gwR>
**Abstract:**
Traditional predictive simulations and remote sensing techniques for forecasting floods are based on fixed and spatially restricted physics-based models. These models are computationally expensive and can take many hours to run, resulting in predictions made based on outdated data. They are also spatially fixed, and unable to scale to unknown areas.
By modelling the task as an image segmentation problem, an alternative approach using artificial intelligence to approximate the parameters of a physics-based model in 2D is demonstrated, enabling rapid predictions to be made in real-time.
- Linux (Windows *may* work but is untested. You will probably have a bad day if you use Windows)
- Node.js (a *recent* version - i.e. v16+ - the version in the default Ubuntu repositories is too old)
- Python 3.8+
- Nvidia GPU (16GiB RAM+ is **strongly recommended**) + CUDA and CuDNN (see [this table](https://www.tensorflow.org/install/source#gpu) for which versions you need)
3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this)
4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data]
5. Push through [HAIL-CAESAR](*https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename)
Only steps #6 and #7 actually use code in this repository. Steps #2 and #4 involve the use of modular [`npm`](https://npmjs.org/) packages.
### Obtaining the data
The data in question is the Met Office's NIMROD 1km rainfall radar dataset, stored in the CEDA archive. It is updated every 24 hours, and has 1 time step every 5 minutes.
The data can be found here: <https://catalogue.ceda.ac.uk/uuid/27dd6ffba67f667a18c62de5c3456350>
There is an application process to obtain the data. Once complete, use the tool `nimrod-data-downloader` to automatically download & parse the data:
`rainfallwrangler` is a Node.js application to wrangle the dataset into something more appropriate for training an AI efficiently. The rainfall radar and water depth data are considered temporally to be regular time steps. Here's a diagram explaining the terminology:
After double checking, rainfallwrangler does NOT mess with the ordering of the data.
## License
All the code in this repository is released under the GNU Affero General Public License unless otherwise specified. The full license text is included in the [`LICENSE.md` file](./LICENSE.md) in this repository. GNU [have a great summary of the licence](https://www.gnu.org/licenses/#AGPL) which I strongly recommend reading before using this software.
