A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine

Liao, Yue; Jian-yu, XU; Wang, Wenjing

doi:10.1016/j.proenv.2011.09.074

Cited by 22 publications

(12 citation statements)

References 6 publications

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“…Figure 3 a shows an optimal hyperplane with a maximising margin between two classes of data. Although the concept of the SVC algorithm was originally based on binary classification, it can be extended to multi-class classification problems by combining a series of binary classifiers [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…The SVR modelling accuracy can be improved with the right choice of kernel function as different kernel functions have different mapping capabilities. The four kernel functions given in Equations (16) to (19) are most commonly used in the SVR algorithm [ 22 , 35 , 36 , 37 , 38 , 39 , 41 ]:

where

is the transpose of

, r is a constant term,

is the polynomial order, and

is a RBF kernel parameter that controls the spread of the data while transforming to higher dimensions.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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Section: Methodsmentioning

confidence: 99%

where

is the transpose of

, r is a constant term,

is the polynomial order, and

is a RBF kernel parameter that controls the spread of the data while transforming to higher dimensions.…”

Section: Methodsmentioning

confidence: 99%

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In this study, support vector machine method has been used for comprehensive evaluation of information with regards to the behavioral differences of organisms in different toxic aqueous environments, for which three different classes of toxicity degree have been developed. This high accuracy of this estimation suggests its suitable efficiency in indirect evaluation of heavy metals in aqueous environments (Liao et al, 2011).…”

Section: M)mentioning

confidence: 78%

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Faraj¹,

Shen²

2018

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Careful monitoring of the quality and quantity of water resources plays a significant role in modern environmental management. Thus, to achieve this aim, the quantitative and qualitative parameters of water resources should be measured and controlled with a desirable accuracy. However, it is not always possible to measure these parameters with easy, inexpensive, precise, and quick experimental methods. Therefore, today to solve this type of problems, new methods including smart methods are used which have a great potential in many computational areas. Considering the variety of the studies and absence of a comprehensive review paper, this research should be conducted. The aim of this paper is to comprehensively review application of the smart methods of artificial neural network and support vector machine in the area of water resources and to develop a comprehensive study source for the researchers interested in this field. The results of these studies all show that these advanced smart methods are more efficient, accurate, economical, and faster than other computational methods to predict quantitative and qualitative parameters of water resources.

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“…Long-term drug exposure also results in chronic stress. Pittman and Ichikawa developed a video-based movement tracking and analysis system that used iPhones to quantify behavioral changes following psychoactive drug exposure in zebrafish [126]. These apps were able to track multiple animals' social interactions as well as tracking their individual paths, using a sophisticated range of data selection options and wide array of variables to quantify animals' behavior and allow the user to produce accurate descriptions of complex behaviors in diverse situations by visualizing the animals' tracks.…”

Section: Stress Responsementioning

confidence: 99%

Survey of Fish Behavior Analysis by Computer Vision

Niu¹,

Li²,

Peng³

et al. 2018

J Aquac Res Development

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A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine

Cited by 22 publications

References 6 publications

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Survey of Fish Behavior Analysis by Computer Vision

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