Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Round 1

Reviewer 1 Report

The article discusses a current topic, which is particularly applicable in railway transport. The topic of Prediction Models for Railway Track Geometry Degradation using Machine Learning Methods is currently in high demand. This approach can significantly reduce the rate of adverse events in rail transport. The authors present methods that use the potential of computing resources. The given overview of methods is up-to-date, with a logical division. Based on the study of the article, I recommend it for publication.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Review Report - Detailed Comments for Authors

Dear Authors,

we have the following question about your submission to Journal of Sensor (MDPI)

“ Prediction Models for Railway Track Geometry Degradation using Machine Learning Methods: A Review ”

Please, correct / modify your paper according to reviewers’ notes.

The present study focused on prediction models for railway. This study is well conducted, although there are several important problems, which should be revised before publication.

1. Why is your study important?

2. quality of figure 2.a  have problem please replace

3. The conclusion section is missing some perspective related to future research works.

4. proofreading is essential

5. elaborate on the discussions

The manuscript could be substantially improved by relying and citing more on recent literatures.

 

 

Best Regards,

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper reviews the existing prediction methods for railway track degradation, with a special focus on applying machine learning models. I found this study very interesting and informatics.

 I see that the author/s successfully reported the existing prediction method and the advantage and drawbacks of each technique. Furthermore, the manuscript is well written with clear, understandable English and is easy to follow by readers.

Therefore, I recommend that the manuscript be accepted for publication after addressing my comments attached. These comments are relatively minor in nature and, if followed, will improve the paper.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf