A Multifactor-Based Random Forest Regression Model to Reconstruct a Continuous Deformation Map in Xi’an, China

Round 1

Reviewer 1 Report

In this study, K-RFR model is applied to reconstruct the continuous deformation map based on InSAR results and influence factor. Where the K-RFR combing k-means clustering which could reduce the influence of heterogeneity, and random forest regression algorithm which is used in ground deformation prediction. This study is innovative using the RFR model to predict the non-values in InSAR deformation maps, which could provide a better idea to predict the whole deformation map for InSAR results.

 1.     Could you explain the weights of influence factors playing roles in RFR models?

2.     The image quality of Figure 13 should be improved, it should be clearer.

Author Response

Many thanks for your review, my response is given as an attached document.

Author Response File: Author Response.docx

Reviewer 2 Report

In this paper the author used a multifactor-based machine learning model, namely K- 22 RFR model, by combining K-means clustering and random forest regression algorithm, to reconstruct the continuous deformation map.

I have been reading the article cand I consider that the work carried out by the authors is correct and well structured. The graphs are clear and show the information progressively to make it easier for the reader to understand the article. The sections are well structured and the manuscript has all the proper parts of a research paper.

The explanation of the applied methodologies are clearly described and are appropriate for the objective sought by the authors of deformation control. I also consider the results and conclusions correct and adjusted to the methodology and parameters described on this experiment.

In my judgment as a reviewer, I consider this paper appropriate for publication in Remote Sensing. However, since my native language is not English, I advise the editor and authors to thoroughly check for possible grammatical Englesh errors.

Author Response

Reviewer #2:

Review on remotesensing-2572614 (Multifactor-based random forest regression model to reconstruct continuous deformation map in Xi’an, China):

In this paper the author used a multifactor-based machine learning model, namely K- RFR model, by combining K-means clustering and random forest regression algorithm, to reconstruct the continuous deformation map.

I have been reading the article cand I consider that the work carried out by the authors is correct and well structured. The graphs are clear and show the information progressively to make it easier for the reader to understand the article. The sections are well structured and the manuscript has all the proper parts of a research paper.

The explanation of the applied methodologies are clearly described and are appropriate for the objective sought by the authors of deformation control. I also consider the results and conclusions correct and adjusted to the methodology and parameters described on this experiment.

In my judgment as a reviewer, I consider this paper appropriate for publication in Remote Sensing. However, since my native language is not English, I advise the editor and authors to thoroughly check for possible grammatical Englesh errors.

Reply: We appreciate your positive comments and valuable suggestions. We have checked the writing of the manuscripts thoroughly.