abstract

Online anomaly detection for drinking water quality using a multi-objective machine learning approach

Authors:

Victor Henrique Alves Ribeiro,

Gilberto Reynoso-MezaAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1 - 2

https://doi.org/10.1145/3205651.3208202

Published: 06 July 2018 Publication History

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Abstract

This document proposes the use of multi-objective machine learning in order to solve the problem of online anomaly detection for drinking water quality. Such problem consists of an imbalanced data set where events, the minority class, must be correctly detected based on a time series denoting water quality data and operative data. In order to develop two different robust systems, signal processing and feature engineering are used to prepare the data, while evolutionary multi-objective optimization is used for feature selection and ensemble generation. The proposed systems are tested with hold-out validation during optimization, and are expected to generalize well the predictions for future testing data.

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Cited By

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Lima JTavares LPacitti EFerreira JSantos ISiqueira ICarvalho DPorto FCoutinho ROgasawara E(2024)Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650809(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650809
Fan JGe YZhang XWang ZWu HWu J(2024)Learning the feature distribution similarities for online time series anomaly detectionNeural Networks10.1016/j.neunet.2024.106638(106638)Online publication date: Aug-2024
https://doi.org/10.1016/j.neunet.2024.106638
Ribeiro VReynoso-Meza G(2023)Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring ProblemInternational Journal of Information Technology & Decision Making10.1142/S021962202350010423:01(447-474)Online publication date: 13-Feb-2023
https://doi.org/10.1142/S0219622023500104
Show More Cited By

Index Terms

Online anomaly detection for drinking water quality using a multi-objective machine learning approach
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Continuous space search
  2. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2018

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Lima JTavares LPacitti EFerreira JSantos ISiqueira ICarvalho DPorto FCoutinho ROgasawara E(2024)Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650809(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650809
Fan JGe YZhang XWang ZWu HWu J(2024)Learning the feature distribution similarities for online time series anomaly detectionNeural Networks10.1016/j.neunet.2024.106638(106638)Online publication date: Aug-2024
https://doi.org/10.1016/j.neunet.2024.106638
Ribeiro VReynoso-Meza G(2023)Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring ProblemInternational Journal of Information Technology & Decision Making10.1142/S021962202350010423:01(447-474)Online publication date: 13-Feb-2023
https://doi.org/10.1142/S0219622023500104
S HS AV S J(2023)An Efficient Sensory Monitoring & Bacteria Detection System for Contaminated Water2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI58221.2023.10201002(1-6)Online publication date: 25-May-2023
https://doi.org/10.1109/ACCAI58221.2023.10201002
Al-Gunaid MShcherbakov MArtyushin VShkolny DBelov S(2023)Detecting Anomalies in Multidimensional Time Series Using Binary ClassificationCreativity in Intelligent Technologies and Data Science10.1007/978-3-031-44615-3_22(323-336)Online publication date: 14-Oct-2023
https://doi.org/10.1007/978-3-031-44615-3_22
Gündoğdu S(2022)Application of MLR-PRN model for estimation of arsenic concentration in drinking water: a case study for İzmir CityUrban Water Journal10.1080/1573062X.2022.206239519:6(589-599)Online publication date: 14-Apr-2022
https://doi.org/10.1080/1573062X.2022.2062395
Dogo ENwulu NTwala BAigbavboa C(2021)Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly DetectionSymmetry10.3390/sym1305081813:5(818)Online publication date: 7-May-2021
https://doi.org/10.3390/sym13050818
Dogo ENwulu NTwala BAigbavboa C(2020)Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly DetectionIEEE Access10.1109/ACCESS.2020.30386588(218015-218036)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3038658
Ribeiro VReynoso-Meza GAuger AStützle T(2019)Monitoring of drinking-water quality by means of a multi-objective ensemble learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326745(1-2)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326745

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Automatic vs. manual feature engineering for anomaly detection of drinking-water quality

Bayesian Statistical Inference in Machine Learning Anomaly Detection

Pantura Water Quality: Comparing the Pollution Distribution Model of Comal River and Garang River

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