mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-22 09:13:01 +00:00
53 lines
No EOL
3.1 KiB
Markdown
53 lines
No EOL
3.1 KiB
Markdown
# Rainfall Radar
|
|
|
|
> A model to predict water depth data from rainfall radar information.
|
|
|
|
This is the 3rd major version of this model.
|
|
|
|
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.
|
|
|
|
|
|
## System Requirements
|
|
- 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)
|
|
- Experience with the command line
|
|
- 1TiB disk space free
|
|
- Lots of time and patience
|
|
|
|
## Overview
|
|
The process of using this model is as follows.
|
|
|
|
1. Apply for access to [CEDA's 1km rainfall radar dataset](https://catalogue.ceda.ac.uk/uuid/27dd6ffba67f667a18c62de5c3456350)
|
|
2. Obtain rainfall radar data (use [`nimrod-data-downloader`](https://www.npmjs.com/package/nimrod-data-downloader))
|
|
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)
|
|
6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files
|
|
7. Pretrain a contrastive learning model
|
|
8. Encode the rainfall radar data with the contrastive learning model you pretrained
|
|
9. Train the *actual* model to predict water depth
|
|
|
|
Only steps #6 to #9 actually use code in this repository.
|
|
|
|
## rainfallwrangler
|
|
`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:
|
|
|
|
```
|
|
NOW
|
|
│ │ │Water depth
|
|
│▼ Rainfall Radar Data ▼│[Offset] │▼
|
|
├─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┼─┬─┬─┬─┬─┼─┐
|
|
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
|
|
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
|
|
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
|
|
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
|
|
└─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┼─┴─┴─┴─┴─┴─┘
|
|
│
|
|
◄────────── Timesteps ─────────────►
|
|
```
|
|
|
|
Note to self: 150.12 hashes/sec on i7-4770 4c8t, ???.?? hashes/sec on Viper compute
|
|
|
|
After double checking, rainfallwrangler does NOT mess with the ordering of the data. |