DOI: 10.5281/zenodo.4304080
title: GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.

description: Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set. Included in zenodo: 1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv) 2. Call for Participation 3. Rules and Description of the Challenge 4. Resource Package provided to participants 5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv) 6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv) 7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv) 8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv) The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. The competition was organized by: F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln) The dataset was provided by: Thüringer Fernwasserversorgung and IMProvT research project Internet of Things: Online Event Detection for Drinking Water Quality Control Description: For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low. <br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019 Official webpage: https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php