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Article
Peer-Review Record

Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North

by Vida Atashi 1,*, Hamed Taheri Gorji 2, Seyed Mojtaba Shahabi 3, Ramtin Kardan 4 and Yeo Howe Lim 1
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Submission received: 23 April 2022 / Revised: 13 June 2022 / Accepted: 17 June 2022 / Published: 20 June 2022
(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

Round 1

Reviewer 1 Report

For the improvements and corrections to be made, please consider the recommendations in the document attached. There are some minor interventions to make in order to improve the quality and the overall decision to publish the paper.

Comments for author File: Comments.docx

Author Response

In the attached file you may find our responses to your comments.

 

Best

Author Response File: Author Response.docx

Reviewer 2 Report

To Authors:
I thought this article presented a comparison between 3 different methods of forecasting water levels. The result looks acceptable. It could be published, but the only primary concern is the figure quality. I would highly recommend that authors update or modify the figure quality because it is challenging for readers. Especially those axes.

I only have one question about this research article:
1. why does figure 8 show a time shift between LSTM (predicted) and OBS (test)? Any reason for it, or is it the model characteristic? Could it be improved?

Author Response

Thanks for your comments. The attached file is our answers to your comments.

 

Best

Round 2

Reviewer 2 Report

After authors update the figures with better quality and are easy to read. I would recommend accepting this research article. 

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