Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Round 1

Reviewer 1 Report

1.       Author must constructively change the abstract in terms of adding some best numerical values to the result. Must mention method name.

2.       Keywords should be written in alphabetical order.

3.       Please add more recent literature (2023) in terms of runoff in the High-Cold Mountains Area for better understanding.

4.       Please modify the objective section for a clear understanding i.e novelty part should be clearly mentioned.

5.       There are so many techniques in the recent world for the assessment of runoff; why does the author use a specified model for research purposes? Is there any specific reason for this?

6.       Author citation: My suggestion please add some MDPI, Nature journal

7.       Author used Yurungkash and Kalakash Rivers area for research purposes, is there any scientific reasons for this?

8.       Study area figure must be changed; in the study area figure, first, the country figure, then the state with the river, then the location with the river basin must be there. Request to draw study area figure using GIS

9.       Author must add statistical components/parameters of collected data in the study area section.

10.   Here author must be mentioned where they got the data and what is the span of the used data. Is there any specific reason for that?

11.   Comparison statement (compare with other research articles) must be added in the result and discussion section to better visualize the proposed research.

12.   Author must add future scope in the last portion of the manuscript.

13.   Advantages and limitations of the proposed model must be added.

14.   Author must add Research Gap

15.   For better analysis of the result author must add a box plot, and Taylor diagram

16.   Author must provide a flow chart, pseudo code, and architecture of proposed models.

17.   Author must provide a parameter table for clear understanding.

Author Response

Dear Reviewer,

     On behalf of all the contributing authors, I would like to express my sincere gratitude for your letter and your constructive comments on our article entitled "Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models" (Water-2572245), I would like to express my sincere gratitude to you for your letter and the reviewers' constructive comments. These comments were valuable and helped us to improve our article.Based on your comments, we have extensively revised the manuscript and added what was missing in the manuscript to make the results of our article more convincing. In this revised version, changes to our manuscript are highlighted in red in the document. In the annex we have included a point-by-point response to the reviewers' comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

In the submitted manuscript, the authors of the study employed several machine learning algorithms to predict runoff in High-Cold mountains. The authors further used various inputs to accurately estimate the runoff values. They investigated correlation coefficients and the feature importance module of the Random Forest algorithm in order for determining the contribution of the attributes to the runoff predictions. Regarding this study, I listed my comments as follows:

·      The authors should provide a table illustrating the descriptive statistics of the utilized input and output data. Also, you can present the time series of the corresponding dataset.

·      Please provide more explanation regarding the Random forest feature selection technique to make it more reproducible by interested scholars.

·      How the hyperparameters of the utilized machine learning algorithms were tuned? In what ranges they scanned? How the authors are avoided potential overfitting phenomenon.

·      Provide a correlation matrix under Section 4.1.1 and explain how you dealt with  multicollinearity, if any.

·      The authors are strongly recommended to enhance the Discussion section with the findings attained in the past literature.

·      The authors provided some information regarding the reason why the models were incapable of capturing the extreme values using machine learning techniques. However, one would like to see that how the performance of these models can be increased to accurately estimate the runoff extremes.

·      Why the authors did not implement one of the widely employed preprocessing methodologies, namely signal processing techniques? How the results would be affected if the corresponding methods are applied?

Author Response

Dear Reviewer, On behalf of all the contributing authors, I would like to express my sincere gratitude for your letter and your constructive comments on our article entitled "Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models" (Water-2572245), I would like to express my sincere gratitude to you for your letter and the reviewers' constructive comments. These comments were valuable and helped us to improve our article. Based on your comments, we have extensively revised the manuscript and added what was missing in the manuscript to make the results of our article more convincing. In this revised version, changes to our manuscript are highlighted in red in the document. The attached document contains our point-by-point response to the comments you have made.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Thanks for addressing my comments. The paper can be accepted in its current form.