Datasetpublic

xairon/piezo-embedding-benchmark

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 Dataset overview, label distributions, geographic maps, time series examples 02_benchmark_analysis.ipynb Encoder comparison, whitening effect, uni vs multi, ranking Dataset Description This dataset supports… See the full description on the dataset page: https://huggingface.co/datasets/xairon/piezo-embedding-benchmark.

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Dataset Card

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	Dataset overview, label distributions, geographic maps, time series examples
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 (niveaunappeeau)
—Multivariate daily series (2000 stations, ~10.6M rows): groundwater level + 3 ERA5 covariates (temperature2m, totalprecipitation, potential_evaporation)

Files

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

Source

—Groundwater data: BRGM HubEau API
—Climate data: ERA5 reanalysis (Hersbach et al., 2020)
—Labels: BDLISA hydrogeological environments

Usage

Associated Repository

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