GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
Authors/Creators
- 1. Institute for Data Science, Engineering, and Analytics, Technische Hochschule Köln
Description
Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany
The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.
Included in zenodo:
- dataset of water quality data
- additional material and descriptions provided for the competition
The competition was organized by:
M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)
The dataset was provided and prepared by:
Thüringer Fernwasserversorgung,
IMProvT research project (S. Moritz)
Industrial Challenge: Monitoring of drinking-water quality
Description:
Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.
Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.
Submission deadline:
June 30, 2017
Official webpage:
http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/