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Drinking Water Engineering and Science
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A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant in Al Qusayr. Several models built and fitness detected for each, the network with the highest fitness selected, then the network was constructed. The network was able to predict turbidity with high accuracy, R (0. 9940) and a RMSE of 0.1078. And 4 equations for determining the value of the residual aluminum was obtained using Gene expression, the best equation results were, RMSE = 0.02, R = 0.9.
A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant...
Preprints
https://doi.org/10.5194/dwes-2021-8
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/dwes-2021-8
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
31 Mar 2021
31 Mar 2021
Review status: this preprint is currently under review for the journal DWES.
Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP
- 1Engineer at the Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
- 2Prof. Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
- 3Prof. Department of Engineering Management and Construction, Faculty of Civil Engineering, Damascus University, Damascus, Syria
- 1Engineer at the Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
- 2Prof. Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria
- 3Prof. Department of Engineering Management and Construction, Faculty of Civil Engineering, Damascus University, Damascus, Syria
Abstract. Turbidity is the most important parameter needed to check the status of drinking water, as it is an integrated parameter because its high values indicate high values of other parameters related to water quality. Coagulation and flocculation are the most essential processes for the removal of turbidity in drinking water treatment plants. Using alum coagulants increases the aluminum residuals in treated water, which have been linked to Alzheimer's disease pathogenesis.
In this paper, a hybrid algorithm (GA-ANN) used to predict the turbidity values in the drinking water purification plant in Al Qusayr was used.
The models were constructed using raw water data: turbidity of raw water, pH, conductivity, temperature, and coagulant dose, to predict the turbidity values coming out of the plant.
Several models built and fitness detected for each model, the network with the highest fitness was selected, and then a hybrid prediction network was constructed.
The selected network was the most able to predict turbidity of the outlet with high accuracy with a correlation coefficient (0. 9940) and a root mean square error of 0.1078.
And 4 equations for determining the value of the residual aluminum was obtained using Gene expression method, and the best equation produced results with very good accuracy, in this regard it can be referred to RMSE = 0.02 R = 0.9 for the best model.
How to cite. Alsaeed, R., Alaji, B., and Ebrahim, M.: Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP, Drink. Water Eng. Sci. Discuss. [preprint], https://doi.org/10.5194/dwes-2021-8, in review, 2021.
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Ruba Alsaeed et al.
Status: open (until 03 Jul 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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CC1: 'Comment on dwes-2021-8', fadi Asker, 02 Apr 2021
reply
The article in general is good written and it is about a very important issue related to drinking water treatmentI noticed that the refrences are quite new. And tge equation gives the article more stringth, likewise the GUI .
But I have a question why the researcher used Ga and Ann and GEP
not fuzzy for example?
And at the end of the article I think it is good to mention the other 3 equation that reselted from the research, not just the best one
GOOD LUCK
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AC1: 'Reply on CC1', Ruba Alsaeed, 02 Apr 2021
reply
We used GA-ANN Because there is some articles on the internet about predicting turbidity and other parameters by Fuzzy logic
So to do something new , we used ANN Hybrid with GA, GA was used because many researches in other fields said that GA can improve the structure of ANN. And it gave great results comparing to similar researches.
And for the other 3 equation, I can provide them in the final copy if I am allowed to add something to the article.
GEP was great tool for predicting in a very amazing way.
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AC1: 'Reply on CC1', Ruba Alsaeed, 02 Apr 2021
reply
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CC2: 'Comment on dwes-2021-8', Salim Heddam, 03 Apr 2021
reply
In the present work the authors propose and application of data driven approaches for predicting water turbidity and residual aluminum in drinking water treatment plant. The paper is within the scope of the journal, few similar studies can be found in the literature especially regarding the prediction of residual aluminum which is very interesting. However, the paper with the actual form is with low quality and needs to be in depth amended and reorganized before to be ready for publication.
- English is below the required level, and the paper is hard to read and to understand what is really done by the authors. Native English speaker can help in improving the English style of the paper.
- From the abstract to the conclusion, the paper needs to be completely reorganized, structured and presented in a clear manner. The actual form is unacceptable, and the paper is without a clear ideas.
- Poor literature review and missing research gap.
- Theoretical description of the models is out of scope.
- Some information’s were provided by the authors and not related to the investigation, i.e., the GRNN model (line 145)???
- Results should be deeply analyzed and discussed.
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AC3: 'Reply on CC2', Ruba Alsaeed, 12 Apr 2021
reply
Thank you very much for your valuable notices, I have adjusted the literature review
to be more clear and to summarize the previous studies in this field more clear.it is shown in the attached file.
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AC4: 'Reply on CC2', Ruba Alsaeed, 12 Apr 2021
reply
Thank you very much for your valuable notices, I have adjusted the literature review
to be more clear and to summarize the previous studies in this field more clear.it is shown in the attached file.
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CC3: 'Comment on dwes-2021-8', Lina khouri, 04 Apr 2021
reply
In this paper, a Hybrid network (GA - ANN) which is a data-driven method, was proposed for predicting water turbidity, and GEP which is a promising tool in many fields, was used for the prediction of residual aluminum in drinking water treatment plants. There are a few similar studies in scientific journals, particularly for the prediction of residual aluminum, which was done in this study, and it is a very interesting topic and related to the journal reach.
- The English language should be checked by native English speaker .
- I think the abstract could be rearranged to give more information about the article.
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AC2: 'Reply on CC3', Ruba Alsaeed, 07 Apr 2021
reply
I was obligated to a certain number of words in the abstract, I will take this notice into consideration
Thank you
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AC2: 'Reply on CC3', Ruba Alsaeed, 07 Apr 2021
reply
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CC4: 'Comment on dwes-2021-8', Lilas Nabhan, 12 Apr 2021
reply
The research is good and has innovative ideas
The issue is also important for water purification plants due to the importance of turbidity in assessing the stability of the plant’s work and controlling aluminum values
But it needs some modification and revision, especially with regard to the language, and it is preferred to be reviewed by a native English speaker
And although that there are some new references, It is also preferable to add other reference studies in the foreground-
AC5: 'Reply on CC4', Ruba Alsaeed, 14 Apr 2021
reply
Thank you very much for your valuable notices, I have adjusted the literature review so it becomes more clear and summarizes the previous studies in this field.
it is shown in the attached file
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AC5: 'Reply on CC4', Ruba Alsaeed, 14 Apr 2021
reply
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CC5: 'Comment on dwes-2021-8', amin hasanalipou shahrabadi, 13 Apr 2021
reply
This paper is good in overall. The subject of this article is very important todays because of Lack of drinking water resources as well as Existence of pathogenic aluminum particle. In general, the following appear: 1. Almostly new and very interesting subject and new method for solving the problem (there are some article with Fuzzy). This is in scope of the journal. 2. The references are quite new.3. The English language should be checked. I think it will be better.4. The quality of some figs must be better. Ex: fig.85. The abstract must be written in 1 paragraph.
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AC6: 'Reply on CC5', Ruba Alsaeed, 14 Apr 2021
reply
Doctor Amin Hasanalipou Shahrabadi
Thank you for mentioning that methods that were used (GEP and hybrid GA -ANN) were new in solving this kind of problems
as you said there are some articles that worked with Fuzzy systems and GMDH.You are right figure 8 needs to be more clear, I have fixed this
and also adjusted the abstract in one paragraph
I have attached a file showing the changes
thank you
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AC6: 'Reply on CC5', Ruba Alsaeed, 14 Apr 2021
reply
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CC6: 'Comment on dwes-2021-8', Lina khouri, 14 Apr 2021
reply
Good luck
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CC7: 'Comment on dwes-2021-8', Prakash Jagadeesan, 30 Apr 2021
reply
The authors have developed a soft sensor to predict the turbidity level in the drinking water. The three years data from the water purificatoon plant has been used for validation. The efforst taken by the research scholar and his supevisors deserves special appreciation
Wishing the scholarr a very good luck
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CC8: 'Comment on dwes-2021-8', Adnan Sallom, 30 Apr 2021
reply
This paper was well prepared.
The stringth points are:
- There are few articles that studied predicting turbidity and much less for the aluminum prediction.
- Also, using GEP in the field of water treatment plants is very promising.
- Predicting models are very important, especially when they are combined with Early Warning Systems.
- Comparing the results of MLR with ANN was very reliable
cause this parameter have nonlinear relations and it is hard to be described using linear ways
However, this paper still needs some further improvements.
1.There is some grammatical and structural errors.
2.The authors did not discuss the limitations of the data set they used.
3.In figure 3 what is the program used to make this figure?
4.figure 7 needs to be more clear.
The article is suitable for publication after some language editing.-
CC10: 'Reply on CC8', Ruba Alsaeed, 09 May 2021
reply
professor Adanan
the program used to make figure 3 is Orange 7. It helped to match 4 different things
a number of neurons, MSE, the function of the hidden layer, the function of the output layer .
and as you mentioned, figure 7 needs to be more clear
I attached a photo of the edited figure
Thanks for your comments
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AC7: 'Reply on CC8', Ruba Alsaeed, 09 May 2021
reply
professor Adanan
the program used to make figure 3 is Orange 7. It helped to match 4 different things
a number of neurons, MSE, the function of the hidden layer, the function of the output layer .
and as you mentioned, figure 7 needs to be more clear
I attached a photo of the edited figure
Thanks for your comments
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CC10: 'Reply on CC8', Ruba Alsaeed, 09 May 2021
reply
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CC9: 'Comment on dwes-2021-8', Rana Muhammad Adnan Ikram, 02 May 2021
reply
This subject addressed is within the scope of the journal. However, the manuscript in the present version contains several problems. Appropriate revisions should be undertaken in order to justify recommendation for publication. It is mentioned that GA-ANN and GEP models are used. What are the advantages of adopting these particular methods over others in this case? How will this affect the results? More details should be furnished.Why not tried MARS/OP-ELM/DENFIS/GMDH for comparison and validation? For readers to quickly catch your contribution, it would be better to highlight major difficulties and challenges, and your original achievements to overcome them, in a clearer way in abstract and introduction.
Ruba Alsaeed et al.
Ruba Alsaeed et al.
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Short summary
A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant in Al Qusayr. Several models built and fitness detected for each, the network with the highest fitness selected, then the network was constructed. The network was able to predict turbidity with high accuracy, R (0. 9940) and a RMSE of 0.1078. And 4 equations for determining the value of the residual aluminum was obtained using Gene expression, the best equation results were, RMSE = 0.02, R = 0.9.
A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant...
Drinking Water Engineering and Science
An interactive open-access journal
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