Proof of Concept: JUNON Digital Twin (v1.3)

Proof of Concept: JUNON Digital Twin (v1.3)

Context and methodology

JUNON is an ambitious research programme to develop a digital research cluster on the continental environment (agricultural, urban, forestry and river) in the Centre-Val de Loire region, France. This cluster aims to design digital services to improve the monitoring and understanding of the environment, for better management of natural resources.

This repository contains a proof of concept (PoC) of the JUNON Digital Twin. It tests some planned parts of DT, data exchange protocols and communication processes. This PoC is implemented in a single machine that emulates a DATA network. External repositories (ERA5, Hubeau-ADEs and Folium) are connected through official APIs. The developed PoC serves a use case in which the DT collects and manipulates data related to 255 piezometric sensors located in different regions of France. They collect data of aquifer water levels (ADEs). Additionally, this information is combined with measurements and estimations concerning potential evaporation, total precipitation, and temperature at 2m for the same geographic locations (ERA5). Folium repositories are accessed to obtain interactive geographic maps.

The DT offers the visualization of monthly averaged time series from 2000 to 2020 and, additionally, it is able to run clustering and anomaly detection analysis over the 255 mesurement points (each of them represented by a multi-variate time series composed by the four collected variables). Applied advanced machine learning methids include: CLASSIX, SDOclust, and Time Series kMeans with Soft and Barycenter Averaging Dynamic Time Warping metrics. Users access the PoC through a Dashboard.

Technical details

• [assets], folder to save downloaded resources used by the dashboard.
• [database], stores the SQlite database.
• [misc] contains metadata related to geolocated points of measurement (BSS codes).
• [screenshots] contains some screenshots of the dashbooard after clustering data with the different implemented methods.
• [utils] contains additional scripts related to the database, API access and clustering libraries.
• "app.py" runs the dashboard.
• "download_background_data.py" runs the process that downloads raw data from official servers.
• "README.md" contains the documentation.
• "setup.py" sets up the environment for building the database.

All data files are licensed under CC BY 4.0, all software is licensed under MIT License.