BDE SC5 PILOT #3

The pilot is carried out by NCSR-D in the frame of SC5 Climate Action, Environment, Resource Efficiency and Raw Materials.

The pilot demonstrates the following workflow: A (potentially hazardous) substance is released in the atmosphere that results to increased readings in one or more monitoring stations. The user accesses a user interface provided by the pilot to define the locations of the monitoring stations as well as a timeseries of the measured values (e.g. gamma dose rate). The platform initiates

• a weather matching algorithm, that is a search for similarity of the current weather and the pre-computed weather patterns, as well as
• a dispersion matching algorithm, that is a search for similarity of the current substance dispersion patterns with the precomputed ones.
• Semanticly-aware querying to enrich dispersion patterns with additional information such as affected population and nearby hospitals
• A uniform perspective of heterogeneous data stored in heterogeneous data management and processing infrastructures using SemaGrow.

Demo

Setup

$ cd sc5_sextant-docker
$ docker build . -t sc5_sextant
$ cd ..
$ docker-compose up -d
$ cd osm-docker
$ docker-compose up -d
$ cd ../geonames-docker
$ docker-compose up -d

RUN

After data ingestion
$ docker -D exec -it sc5_sextant bash /pilot-sc5-cycle3/backend/sc5_backend.sh

Info

• netcdf_weather_files: NetCDF files containing 3 days worth of six hours time frames. These files are used as the current weather in order to perform source estimation.

  •     Recommended data sources: ECMWF,NCAR
        Recommended structure: ERA-Interim

• netcdf_dispersion_files: NetCDF files that contain dispersions for different clustering methods,descriptors and configurations. Usually these files are the output of an atmospheric dispersion model i.e HYSPLIT,DIPCOT.

• model_template.zip: zip files that contain the NeuralNetwork model that is used for source estimation. Usually exported from model_template class and neural network scripts.

• clustering_method: Clustering configuration i.e shallow_ae (Single autoencoder), deep_ae (Stacked autoencoders), etc.

• descriptor : descriptor used for clustering_method i.e km2 (double kmeans), dense (density-based descriptors).

• html_repr: String that presents the estimation method to the end user.