46 lines
2.2 KiB
Markdown
46 lines
2.2 KiB
Markdown
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# eSPA for RTS
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Goal of this project is to utilize the entropy-optimal Scalable Probabilistic Approximations algorithm (eSPA) to create a model which can estimate the density of Retrogressive-Thaw-Slumps (RTS) across the globe with different levels of detail.
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Hoping, that a successful training could gain new knowledge about RTX-proxies.
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## Setup
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```sh
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uv sync
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```
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## Project Plan
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1. Create global hexagon grids with h3
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2. Enrich the grids with data from various sources and with labels from DARTS v2
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3. Use eSPA for simple classification: hex has [many slumps / some slumps / few slumps / no slumps]
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4. use SPARTAn for regression: one for slumps density (area) and one for total number of slumps
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### Data Sources and Engineering
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- Labels
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- `"year"`: Year of observation
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- `"area"`: Total land-area of the hexagon
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- `"rts_density"`: Area of RTS divided by total land-area
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- `"rts_count"`: Number of single RTS instances
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- ERA5 (starting 40 years from `"year"`)
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- `"temp_yearXXXX_qY"`: Y-th quantile temperature of year XXXX. Used to enter the temperature distribution into the model.
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- `"thawing_days_yearXXXX"`: Number of thawing-days of year XXXX.
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- `"precip_yearXXXX_qY"`: Y-th quantile precipitation of year XXXX. Similar to temperature.
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- `"temp_5year_diff_XXXXtoXXXX_qY"`: Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 5 years difference.
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- `"temp_10year_diff_XXXXtoXXXX_qY"`: Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 10 years difference.
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- `"temp_diff_qY"`: Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 10 years difference.
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- ArcticDEM
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- `"dissection_index"`: Dissection Index, (max - min) / max
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- `"max_elevation"`: Maximum elevation
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- `"elevationX_density"`: Area where the elevation is larger than X divided by the total land-area
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- TCVIS
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- ???
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- Wildfire???
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- Permafrost???
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- GroundIceContent???
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- Biome
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**About temporals** Every label has its own year - all temporal dependent data features, e.g. `"temp_5year_diff_XXXXtoXXXX_qY"` are calculated respective to that year.
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The number of years added from a dataset is always the same, e.g. for ERA5 for an observation in 2024 the ERA5 data would start in 1984 and for an observation from 2023 in 1983.
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