Document Type : Research article
Authors
1 MS.c Student, Environmental Health Engineering, Birjand University of Medical Sciences, birjand, Iran.
2 Associate Professor, Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran.
3 Assistant Professor Department of Environmental Health Engineering, Ferdows School of Allied Medicine and Public Health, Birjand University of Medical Sciences, Birjand, Iran.
Abstract
Abstract
Background and purpose
The turbidity of treated water is measured as an important parameter in determining the quality of drinking or industrial water in all treatment plants. Due to the importance of the prevalence of pathogens such as Giardia and Cryptosporidium, which cause dangerous diseases such as dysentery, the relationship between reducing turbidity and increasing the elimination of these microorganisms has been proven in studies.
Materials and methods
In this study, an artificial neural network (ANN) model and multiple linear regression(MLR) were developed and their performance was compared to predict the turbidity of treated water of Tabas water treatment plant. Total dissolved solids, pH, temperature and input turbidity of raw water were used as input parameters of the models in the predictions. The best backpropagation algorithm and number of neurons were determined to optimize the model architecture.
Results
The results showed that the Levenberg–Marquardt algorithm was selected as the best algorithm and the number of optimal neurons was determined to be 16.
Also, the results of the sensitivity analysis of the neural network model showed that the input turbidity with a value of 29% is the most important parameter in the development of the ANN model.
Conclusion
The results of correlation coefficient of MLR and ANN models were obtained for training data 0.63 and 0.8921 and for testing data 0.60 and 0.8571, respectively, which show the superiority of ANN model in Predicting the turbidity of the output of Tabas water treatment plant.
Keywords
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