Artificial Neural Networks in the Prediction and Assessment for Water Quality: A Review

Chen, Yingyi; Fang, Xiaomin; Yang, Ling; Liu, Yeqi; Gong, Chuanyang; Di, Yuqi

doi:10.1088/1742-6596/1237/4/042051

Cited by 6 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last hidden layer sends its output signal to the output layer. The final output layer decodes the signal into a final response to the original stimulus (the input) (Chen et al 2019). RBF is also known as a localized receptive field network because the basic functions in the hidden layer produce a significant nonzero response to input stimulus only when the input falls within a small, localized region of the input space (Lee and Chang 2003).…”

Section: Prediction Of Do Using Annsmentioning

confidence: 99%

Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique

Bhardwaj¹,

Singh²

2022

JWMM

View full text Add to dashboard Cite

Water quality modeling with dynamic parameters, especially of rivers, is important in terms of proactive pollution management strategies. Techniques such as artificial neural networks (ANNs) have become popular for such applications. In the present study, an ANN is used to construct a multilayer perceptron and radial basis function neural network model to simulate and predict dissolved oxygen in the River Ganga in selected regions of Uttar Pradesh, and to demonstrate its application in identifying complex nonlinear relationships between input and output variables. The results of the model analysis demonstrate that the multi-layer perceptron model provides greater correlation coefficients (R = 0.993) and a lower mean square error (RMSE = 0.1984) than the radial basis function model (R = 0.789; RMSE = 1.0011). The results of the analysis suggest the suitability of the proposed MLP-ANN model to predict water quality parameters such as dissolved oxygen using limiting data sets for the River Ganga, in particular, and other rivers in general.

show abstract

Section: Prediction Of Do Using Annsmentioning

confidence: 99%

Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique

Bhardwaj¹,

Singh²

2022

JWMM

View full text Add to dashboard Cite

show abstract

“…A simple rule of thumb maybe fulfills the purpose, that is random numbers must lie in the boundary between -2/I to 2/I, where I are inputs provided to the artificial neural network in any given node [12]. Many of the recent water quality studies are done by implying Artificial Neural Networks such as [13], [14], [15], [16] , [17] , [18] , [19] ,[20] , [21], [22] etc. In this study the Physico Chemical Water Quality of Manora Channel is assessed by the assessment of water quality parameter Ammonia.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Implementations on the coastal water quality of Manora channel for the years 1996 to 2014.

Ghayas

Siddiquie

Safdar

et al. 2020

International Journal of Circuits, Systems and Signal Processing

View full text Add to dashboard Cite

Neural Networks is an Important Part of Computational Intelligence, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. Sea water quality contaminates due to the severe untreated domestic, sewage and industrial pollutants. Presence of ammonia in seawater causes the deterioration of coastal water in terms of diminution of oxygen levels which suffocates the marine lives, fishes and mangroves. Industrial, sewage and domestic effluents carried by Lyari River contaminate the Manora channel, Karachi. The aim of study is to make the clear and transparent step-wise use of Artificial Neural Networks for the data driven water quality parameters models of Manora channel (Lyari river outfall zone N 24-51-26, E 66-58-01), Karachi (Pakistan) as well as to compare the pollutant contaminant ratio with the national environmental quality standard limits and other sampling sites of Manora channel and southern east Karachi coast. In this study, Manora channel Physico-chemical water quality parameters are assessed by using Artificial Neural Network taking Biochemical Oxygen Demand (BOD), chemical oxygen Demand (COD), Bicarbonates, potential Hydrogen(pH) , Chloride(Cl) as input and Ammonia(NH3)as output. Mean Square Error and R square are used for the model assessments statistical metrics. The computational work has been done by using R-studio. This is also found that Manora channel has the contaminated level of ammonia along the other sampling stations of both southern Karachi coast (N 24-47-03 E 67-08-39) as well as the other sampling site of Manora channel Karachi coast (N 24-50-15, E 66-58-01). In spite of all contamination Ammonia is found to be within National Environmental Quality Standards limits of Pakistan.

show abstract

“…In short, an artificial neural network (ANN) is one of the most well-known nonlinear calibration techniques that can model almost all kinds of complex nonlinear data on the components of interest. ANN has been applied in numerous prediction and classification studies [32]- [34]. However, both overfitting and underfitting must be avoided to fully reveal the capabilities of ANN using appropriate validation approaches.…”

mentioning

confidence: 99%

Artificial Neural Network and Near Infrared Light in Water pH and Total Ammonia Nitrogen Prediction

Suarin

Lee

Chia

et al. 2022

IJIE

View full text Add to dashboard Cite

Water quality plays an important role in aquaculture. The operation of a freshwater aquaculture fish farming is highly dependent on the ability to understand, monitor, and control the physical and chemical constituents of the water. pH and total ammonia nitrogen (TAN) levels are two critical water quality parameters that affect fish growth rate and health. However, pH and TAN levels are affected by uncontrollable factorse.g.weather, temperature, and biological processes occurring in the water. Therefore, it is important to monitor changes in pH and TAN levels frequently to maintain optimal conditions for freshwater habitats. Near infrared spectroscopy (NIR) has been extensively investigated as an alternative measurement approach for rapid quality control without sample preparation. Therefore, this research aims to evaluate the feasibility of machine learning combined with NIR light in predicting the water pH and TAN values of a fish farming system. The proposed system contains three main components i.e.a multi-wavelength light emitting diode (LED), a light sensing element, and a machine learning model i.e.artificial neural network (ANN). First, the transmitted NIR light with different wavelengths of water samples was measured using the proposed system. Then, the actual pH and TAN values of the water samples were quantified using conventional methods. Next, ANN was used to correlate the measured NIR transmittance with the pH and TAN values. The results show that ANN with four hidden neurons achieved the best prediction performance with a mean square error (MSE) of 0.1466 and 0.3136 and a correlation coefficient (R) of 0.8398 and 0.9560 for the pH and TAN predictions, respectively. These results show that ANNcoupled with NIR light can be promisingly developed for in situ water quality prediction without sample preparation.

show abstract

Artificial Neural Networks in the Prediction and Assessment for Water Quality: A Review

Cited by 6 publications

References 22 publications

Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique

Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique

Neural Network Implementations on the coastal water quality of Manora channel for the years 1996 to 2014.

Artificial Neural Network and Near Infrared Light in Water pH and Total Ammonia Nitrogen Prediction

Contact Info

Product

Resources

About