A review of the application of machine learning in water quality evaluation

Zhu, Mengyuan; Wang, Jiawei; Xiao, Yang; Zhang, Yu; Zhang, Linyu; Ren, Hongqiang; Wu, Bing; Ye, Lin

doi:10.1016/j.eehl.2022.06.001

Cited by 250 publications

(122 citation statements)

References 106 publications

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“…ML technique is widely used for predicting unknown objects or analysis of the model performance (Elgeldawi et al, 2021;Haghiabi et al, 2018;Zhu et al, 2022). Recently, several studies have utilized this technique for estimating the model uncertainty with a 95% CI.…”

Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

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Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

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“…MFC-based sensors have been used across multiple industries including the medical, environmental, and robotics shown in Figure 1 A. The main applications, however, lie in the environmental sensors used in water and wastewater treatment [ 93 , 94 ].…”

Section: Sensor Development Using Mets and Hydrogelmentioning

confidence: 99%

Recent Implementations of Hydrogel-Based Microbial Electrochemical Technologies (METs) in Sensing Applications

Wang

Shi

et al. 2023

Sensors

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Hydrogel materials have been used extensively in microbial electrochemical technology (MET) and sensor development due to their high biocompatibility and low toxicity. With an increasing demand for sensors across different sectors, it is crucial to understand the current state within the sectors of hydrogel METs and sensors. Surprisingly, a systematic review examining the application of hydrogel-based METs to sensor technologies has not yet been conducted. This review aimed to identify the current research progress surrounding the incorporation of hydrogels within METs and sensors development, with a specific focus on microbial fuel cells (MFCs) and microbial electrolysis cells (MECs). The manufacturing process/cost, operational performance, analysis accuracy and stability of typical hydrogel materials in METs and sensors were summarised and analysed. The current challenges facing the technology as well as potential direction for future research were also discussed. This review will substantially promote the understanding of hydrogel materials used in METs and benefit the development of electrochemical biosensors using hydrogel-based METs.

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“…Machine-learning can model complex nonlinear relationships without explicitly describing the treatment process using mathematical or chemical equations (Zhu et al, 2022;Azimi et al, 2022). Unlike legacy models, it has great potential to explore a new understanding of wastewater behavior (Xie et al, 2022;Wang et al, 2021;, which is challenging to observe.…”

Section: Introductionmentioning

confidence: 99%

Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms

Qambar

Khalidy

2023

Engineering Applications of Artificial Intelligence

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A review of the application of machine learning in water quality evaluation

Cited by 250 publications

References 106 publications

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

Recent Implementations of Hydrogel-Based Microbial Electrochemical Technologies (METs) in Sensing Applications

Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms

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