IoT 23(4): e2

Research Article

Water Quality Estimation and Anomaly Detection: A Review

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  • @ARTICLE{10.4108/eetiot.v9i4.3660,
        author={Deniz Balta and Seda Balta Ka\`{e} and Musa Balta and S\'{y}leyman Eken},
        title={Water Quality Estimation and Anomaly Detection: A Review},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={9},
        number={4},
        publisher={EAI},
        journal_a={IOT},
        year={2023},
        month={10},
        keywords={Water quality, Anomaly detection, Water management systems, Water analytics, Water distribution networks, Machine learning},
        doi={10.4108/eetiot.v9i4.3660}
    }
    
  • Deniz Balta
    Seda Balta Kaç
    Musa Balta
    Süleyman Eken
    Year: 2023
    Water Quality Estimation and Anomaly Detection: A Review
    IOT
    EAI
    DOI: 10.4108/eetiot.v9i4.3660
Deniz Balta1, Seda Balta Kaç2, Musa Balta1, Süleyman Eken2,*
  • 1: Sakarya University
  • 2: Kocaeli Üniversitesi
*Contact email: suleyman.eken@kocaeli.edu.tr

Abstract

Critical infrastructures that provide irreplaceable services are systems that contain industrial control systems (ICS) that can cause great economic losses, security vulnerabilities and disruption of public order when the information in it is corrupted. These ICSs, which were previously isolated, have now become systems that contain online sensors, wireless networks and artificial intelligence technologies. This situation has also increased the scope of attacks by malicious people who intend to carry out industrial espionage and sabotage these systems. In this study, water quality estimation systems and anomaly detection are comprehensively examined. In this direction, the statistics of the studies in the literature, the methods for water quality anomaly detection, the existing data sets, and the difficulties encountered in the water systems to achieve better water management are discussed. Principle findings of this research can be summarized as follows: (i) new methodologies and architectures have improved water quality assessment through anomaly detection, (ii) different datasets including multi-modal information have been presented, and (iii) remaining challenges and prospects have been investigated.