---
license: apache-2.0
task_categories:
  - time-series-forecasting
  - feature-extraction
  - tabular-classification
language:
  - fr
tags:
  - groundwater
  - piezometry
  - time-series
  - embedding
  - hydrology
  - ERA5
  - BRGM
size_categories:
  - 1M<n<10M
---

# Piezometric Embedding Benchmark Dataset

Daily groundwater level time series from ~4200 French monitoring stations,
with ERA5 climate covariates and hydrogeological labels.

## Notebooks

| Notebook | Description |
|----------|-------------|
| [01_data_exploration.ipynb](notebooks/01_data_exploration.ipynb) | Dataset overview, label distributions, geographic maps, time series examples |
| [02_benchmark_analysis.ipynb](notebooks/02_benchmark_analysis.ipynb) | Encoder comparison, whitening effect, uni vs multi, ranking |

## Dataset Description

This dataset supports the comparative evaluation of time series embedding
methods for piezometric groundwater stations. It contains:

- **Station metadata** (4210 stations): coordinates, hydrogeological labels (milieu_eh),
  department, altitude, and derived statistics
- **Univariate daily series** (2000 stations, ~10.6M rows): groundwater level (niveau_nappe_eau)
- **Multivariate daily series** (2000 stations, ~10.6M rows): groundwater level +
  3 ERA5 covariates (temperature_2m, total_precipitation, potential_evaporation)

## Files

| File | Rows | Size | Description |
|------|------|------|-------------|
| data/station_metadata.parquet | 4,210 | 258 KB | Station coordinates, labels, properties |
| data/piezo_daily_uni.parquet | 10.6M | 38 MB | Univariate daily groundwater level |
| data/piezo_daily_multi.parquet | 10.6M | 119 MB | Multivariate (level + 3 ERA5 covariates) |

## Source

- Groundwater data: [BRGM HubEau API](https://hubeau.eaufrance.fr/page/api-piezometrie)
- Climate data: [ERA5 reanalysis](https://doi.org/10.1002/qj.3803) (Hersbach et al., 2020)
- Labels: [BDLISA](https://bdlisa.eaufrance.fr/) hydrogeological environments

## Usage



## Associated Repository

Full benchmark code, trained models, and analysis notebooks:
https://scm.univ-tours.fr/ringuet/aida_embedding_benchmark

## Citation