scholarly journals Using Principal Component Scores and Artificial Neural Networks in Predicting Water Quality Index

10.5772/32577 ◽  
2012 ◽  
Author(s):  
Rashid Atta ◽  
Sharifuddin M. ◽  
Hafizan Juahir ◽  
Mohd Kamil ◽  
Tg Hanidza
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Artificial Neural ◽  
Component Scores

scholarly journals Use of ultraviolet–visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index

2018 ◽  
Vol 190 (6) ◽  
Author(s):  
Edson Marcelino Alves ◽  
Ramon Juliano Rodrigues ◽  
Caroline dos Santos Corrêa ◽  
Tiago Fidemann ◽  
José Celso Rocha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Water Quality Index ◽  
Quality Index ◽  
Visible Spectrophotometry ◽  
Artificial Neural

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Descriptive screening and lexicon development of non‐aged artisanal cachaça sensorial profile using principal component analysis and Kohonen artificial neural networks

2021 ◽  
Author(s):  
Daniela Caetano ◽  
Clara M. G. Lima ◽  
Ananda L. Sanson ◽  
Débora F. Silva ◽  
Guilherme Souza Hassemer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Artificial Neural

Artificial neural network based water quality index (WQI) for river Godavari (India)

2021 ◽  
Author(s):  
Jyotiprakash G. Nayak ◽  
L.G. Patil ◽  
Vinayak K. Patki
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Quality Index ◽  
Quality Index ◽  
Artificial Neural ◽  
River Godavari

scholarly journals Draw on artificial neural networks to assess and predict water quality

2020 ◽  
Vol 612 ◽  
pp. 012028
Author(s):  
A Fernandes ◽  
H Chaves ◽  
R Lima ◽  
J Neves ◽  
H Vicente
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Artificial Neural
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