A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Round 1

Reviewer 1 Report

The manuscript is very poor in language and organization. The authors should focus on the recent developments, progresses, restrictions, and shortcomings of advanced AI methods in dealing with water quality parameters. Actually, this paper is such a long introduction, not a review paper.  

 

Therefore, I do NOT recommend the article for publication in the Journal of Environment.

Author Response

Reviewer #1 comment:

 

The manuscript is very poor in language and organization. The authors should focus on the recent developments, progresses, restrictions, and shortcomings of advanced AI methods in dealing with water quality parameters. Actually, this paper is such a long introduction, not a review paper. 

Authors’ comment: Thank you very much for your feedback. We believe the paper now is much better. All these issues are amended as below (all amended items are highlighted).

1. The language has now been thoroughly revised.
2. In the current version of the paper, we have clarified the methodology of the hybrid model by using the same classifications in Hajirahimi and Khashei (2022). The edits related to this point can be found in:

• Introduction section 1, lines 105-11
• Figure 3.
• Section 6.

We thank the reviewers for this suggestion, as we feel now that the methodology is much more clearly elaborated in the paper.

Hajirahimi, Z.; Khashei, M. Hybridization of hybrid structures for time series forecasting: a review. Artificial Intelligence Review 2022, 10.1007/s10462-022-10199-0, doi:10.1007/s10462-022-10199-0.

3. Yes, we have. In this version of the submission (section 3), we made some amendments by adding more models that were used in water quality prediction. It also included comparing the advantages and disadvantages of almost common applied models.

Author Response File: Author Response.docx

Reviewer 2 Report

This review manuscript reviews the state of the art regarding procedures for the prediction and modeling of water quality parameters, including their advantages and disadvantages. The important and innovative information collected in this (128 citations -almost 90% of them from the last 5 years-) is of great interest. The review shows different approaches to model the problem and provides valuable proposals for future research in the area of prediction of water quality variables.

The manuscript is well structured and easy to read. That is why I consider that this manuscript meets the conditions to be published in its present form.

Author Response

This review manuscript reviews the state of the art regarding procedures for the prediction and modeling of water quality parameters, including their advantages and disadvantages. The important and innovative information collected in this (128 citations -almost 90% of them from the last 5 years-) is of great interest. The review shows different approaches to model the problem and provides valuable proposals for future research in the area of prediction of water quality variables.

The manuscript is well structured and easy to read. That is why I consider that this manuscript meets the conditions to be published in its present form.

Authors’ comment: Many thanks for your positive opinion about the manuscript.

Author Response File: Author Response.docx

Reviewer 3 Report

This MS aims to categorize the hybrid models proposed for water quality modelling and forecasting, and to analyse articles by considering the unique characteristics of hybrid. The MS is too long. I would suggest publishing the MS in two parts, one for each objective. This would require a step back to reorganize the MS, but it would help to be more concise and straightforward.

Nonetheless, the publication of the MS should be reconsidered after minor revisions.

Specific comments:

Figure 1 Should present to absolute number of papers instead of being presented as a percentage. The same for Figure 2.

Section 3 seems too vague. The subsections should focus more on how such approaches can contribute to the aim of the MS; however, most of the text is dedicated to the description of each method.

Table 4 is wrongly numbered.

Author Response

This MS aims to categorize the hybrid models proposed for water quality modelling and forecasting, and to analyse articles by considering the unique characteristics of hybrid. The MS is too long. I would suggest publishing the MS in two parts, one for each objective. This would require a step back to reorganize the MS, but it would help to be more concise and straightforward.

Nonetheless, the publication of the MS should be reconsidered after minor revisions.

Authors’ comment: Thank you very much for your feedback. We believe the paper now is much better. All these issues are amended as below (all amended items are highlighted).

1. The language has now been thoroughly revised.
2. In the current version of the paper, we have clarified the methodology of the hybrid model of the study by using the same classifications in Hajirahimi and Khashei (2022). The edits related to this point can be found in:

• Introduction section 1, lines 105-11
• Figure 3.
• Section 6.

We thank the reviewers for this suggestion, as we feel now that the methodology is much more clearly elaborated in the paper.

Hajirahimi, Z.; Khashei, M. Hybridization of hybrid structures for time series forecasting: a review. Artificial Intelligence Review 2022, 10.1007/s10462-022-10199-0, doi:10.1007/s10462-022-10199-0.

3. In the current version of the paper, we have made a stronger effort to reduce the total pages of the manuscript from 43 to 31. Thanks

Specific comments:

1. Figure 1 Should present to absolute number of papers instead of being presented as a percentage. The same for Figure 2.

Authors’ comment:  Thank you for pointing this out. This has now been addressed in the current version of the manuscript by adding two y-axes that present the ratio and numbers of papers.

 

2. Section 3 seems too vague. The subsections should focus more on how such approaches can contribute to the aim of the MS; however, most of the text is dedicated to the description of each method.

Authors’ comment: we made some amendments by adding more models that were used in water quality prediction. It also included comparing the advantages and disadvantages of almost common applied models.

 

3. Table 4 is wrongly numbered.

Authors’ comment: This has now been addressed. Thanks.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Accept in present form.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.