mirror of
https://github.com/sbrl/research-rainfallradar
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53 lines
3.1 KiB
Markdown
53 lines
3.1 KiB
Markdown
# Rainfall Radar
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> A model to predict water depth data from rainfall radar information.
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This is the 3rd major version of this model.
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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.
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## System Requirements
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- Linux (Windows *may* work but is untested. You will probably have a bad day if you use Windows)
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- Node.js (a *recent* version - i.e. v16+ - the version in the default Ubuntu repositories is too old)
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- Python 3.8+
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- 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)
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- Experience with the command line
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- 1TiB disk space free
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- Lots of time and patience
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## Overview
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The process of using this model is as follows.
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1. Apply for access to [CEDA's 1km rainfall radar dataset](https://catalogue.ceda.ac.uk/uuid/27dd6ffba67f667a18c62de5c3456350)
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2. Obtain rainfall radar data (use [`nimrod-data-downloader`](https://www.npmjs.com/package/nimrod-data-downloader))
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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)
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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]
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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)
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6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files
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7. Pretrain a contrastive learning model
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8. Encode the rainfall radar data with the contrastive learning model you pretrained
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9. Train the *actual* model to predict water depth
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Only steps #6 to #9 actually use code in this repository.
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## rainfallwrangler
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`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:
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```
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NOW
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│ │ │Water depth
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│▼ Rainfall Radar Data ▼│[Offset] │▼
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├─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┼─┬─┬─┬─┬─┼─┐
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│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
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│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
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│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
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│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
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└─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┼─┴─┴─┴─┴─┴─┘
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│
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◄────────── Timesteps ─────────────►
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```
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Note to self: 150.12 hashes/sec on i7-4770 4c8t, ???.?? hashes/sec on Viper compute
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After double checking, rainfallwrangler does NOT mess with the ordering of the data. |