A Novel Event Detection Model for Water Distribution Systems Based on Data-Driven Estimation and Support Vector Machine Classification

Zou, Xiang-Yun; Yang, Lin; Xu, Bin; Guo, Zi-Bo; Xia, Shengji; Zhang, Tianyang; Wang, Anqi; Gao, Naiyun

doi:10.1007/s11269-019-02317-5

Cited by 20 publications

(9 citation statements)

References 27 publications

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“…Total organic carbon (TOC) is one of the convenient ways of a direct measure of quality as the models can provide data-driven decisions by extracting predictive information from a large dataset. Several studies [7][8][9][10] developed ML models for event detection in water distribution systems. Other studies [11][12][13][14][15][16] investigated the performances of different ML models for predicting water quality parameters of natural source water.…”

Section: Introductionmentioning

confidence: 99%

Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods

Nafsin

2023

Environ. Sci.: Adv.

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Section: Introductionmentioning

confidence: 99%

Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods

Nafsin

2023

Environ. Sci.: Adv.

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“…Macas et al [97] used an "unsupervised attention-based spatio-temporal autoencoder for anomaly detection (STAE-AD)" model to detect attacks against water infrastructures using the SWaT dataset. Zou et al [98] proposed a hybrid model making use of an MLP and a one-class SVM. MLP was used to forecast measurement parameters, and prediction errors were used to train a one-class SVM to classify outliers; finally, Bayesian sequence analysis was used to detect contamination attacks against water distribution systems.…”

Section: Cyber-attack Detection Modelsmentioning

confidence: 99%

A Systematic Review of the State of Cyber-Security in Water Systems

Tuptuk

Hazell

Watson

et al. 2021

Water

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Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems.

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“…For more efficient water management, pollution control, and eliminating the shortcomings of the traditional approaches, machine learning (ML) modeling tools have been developed to predict water quality parameters. ML methods were used for developing event detection models in water distribution systems by Muharemi et al (2018), Perelman et al (2012), Tinelli and Juran (2019), and Zou et al (2019). Other studies (Nafsin et al, 2021; Nafsin & Li, 2021) used statistical methods and event detection system for surface water quality monitoring and analysis.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 5‐day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms

Nafsin

2022

Water Environment Research

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Biochemical oxygen demand (BOD) is one of the most important variables indicating stream pollution with a severe condition of organic loading and maintaining aquatic life in ecosystems. Advanced monitoring techniques such as machine learning (ML) methods have been developed for an accurate, reliable, and cost-effective prediction of BOD. This study investigated the effectiveness of four stand-alone ML algorithms, namely, artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM), and six novel hybrid algorithms, namely, RF-SVM, ANN-SVM, GBM-SVM, RF-ANN, GBM-ANN, and RF-GBM, in predicting BOD of the Buriganga river system of Bangladesh. The feature importance analysis of RF algorithm indicated that chemical oxygen demand (COD), total dissolved solids (TDS), conductivity, total solids (TS), suspended solids (SS), and turbidity are the most influential parameters for predicting BOD 5 . The significance of this study is the application of the novel hybrid models that resulted in higher prediction success; RF-SVM with the highest R 2 value (0.908). The employed novel hybrid ML models can be particularly useful for efficient and systematic data management, water pollution control, and prevention in developing countries such as Bangladesh. Practitioner Points• Investigated the efficiency of four stand-alone and six novel hybrid ML models for predicting BOD in a river of Bangladesh.• The significance of this study is the application of the six novel hybrid models that resulted in higher prediction success.• The best three prediction models were RF-SVM, ANN-SVM, and GBM-SVM with a prediction success of 91%, 89.6%, and 88.8% respectively.• ML models indicated COD, conductivity, TDS, TS, SS, and turbidity as the most influential variables for predicting BOD.• The novel hybrid models can be useful for developing countries for efficient systematic data management, pollution control, and prevention strategies.Nabila Nafsin is a member of the Water Environment Federation (WEF).

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A Novel Event Detection Model for Water Distribution Systems Based on Data-Driven Estimation and Support Vector Machine Classification

Cited by 20 publications

References 27 publications

Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods

Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods

A Systematic Review of the State of Cyber-Security in Water Systems

Prediction of 5‐day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms

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