Deformation Prediction of Cihaxia Landslide Using InSAR and Deep Learning
Round 1
Reviewer 1 Report
Manuscript is well balanced and justified with data and correlations. The LSTM-ARIMA model is need of the hour and requires for real time predictions. Addition with autoregressive models in near future will help scientist for advanced predictions.
Author Response
The notes are in the attached files.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
this paper shows interesting work but not very attractive for the reading public; to be deepened introduction, through more references, and conclusions, in order to better understand the implications and advantages of the proposed method
Author Response
The notes are in the attached files.
Author Response File: Author Response.pdf
Reviewer 3 Report
This paper proposed the LSTM-ARIMA model to predict slope deformation based on the InSAR data. The result shows that the proposed model is robust in slope deformation prediction. However, there are many low-level errors in the article, and major revisions are recommended. Detailed comments are in the following:
1. Page 1, lines 15, The first appearance of professional term abbreviation in the abstract requires full display, i.e, (ARIMA, LSTM).
2. Page 1, lines 15, Author's description the three models, i.e, ARIMA and LSTM are applied in slope deformation prediction first. However, the two models (LSTM) and (ARIMA) are compared in the experiment. What is the third model used?
3. Page 1, lines 16, The author mentioned that the performance of the models is compared and the result shows that LSTM is more effective than ARIMA. Please clarify what performance of the model was specifically compared?
4. It is suggested that the author add “what problems are solved, what methods are proposed, and what advantages are there” to the abstract.
5. Page 1, lines 29, “engineers” changed to “engineering management”.
6. Page 1, lines 32-33, delete “people”. In addition, the article has lots of language problems, and grammatical processing is recommended.
7. Page 5, lines 186-188, it is recommended to align the equal sign in the Eq (8).
8. Please unify whether the Equation expression in the paper is “Eq” or “Equation”.
9. Page 8, lines 279, Figure 4 is too nonstandard. The font in the figure needs to be changed from Chinese to English. At the same time, there is a lack of north arrow, scale and other elements. It is recommended to redraw.
10. Please check all the Figures in the paper, and change the Chinese to English, i.e, Figure 4, 7, 8, 10, 11.
11. What is the “花岗岩” in Table 1? Please avoid the low-level errors.
12. Is the contour on the right side of Figure 11 based on InSAR deformation values on the left side? From the settlement distribution on the left side, it is difficult to draw the figure of the right side, and there's a big difference. Please specify how the graph is derived. In addition, the ranges of the left and right sides in Figure 11 are the same. Why the scales are different.
13. The values of some lines in Figure 14 exceed the range of coordinates. Please draw a complete curve.
14. In the experiment, the author only uses the five points in different zones to establish the model, which is insignificant for the massive deformation observations obtained by InSAR, and there is a suspicion that special points are selected for the experimental results. Please clarify why only five points are used.
15. Page 3, lines 135，Please clarify the source of the real data.
16. Page 5, lines 171，“φ1=φ2” should be corrected to“φobj1=φobj2”.
17. Page 5, lines 176-180，Equation (7) represents only the phase component and does not contain n full cycles of wrapping phase, which cannot reflect the necessity of phase unwrapping, and it is recommended to correct the formula.
18. Page 6, lines 215，a1 a2 a3 … in Equation (14) is necessary to indicate what parameters it means.
19. Page 7, lines 251，please clarify what the key information of landslide deformation captured by the Attention model is ,and whether it has clear physical significance.
Comments for author File: Comments.pdf
Author Response
The notes are in the attached files.
Author Response File: Author Response.pdf
Reviewer 4 Report
The paper entitled "Deformation Prediction of Cihaxia Landslide Using InSAR and 2 Deep Learning" is reviewed. The authors used prepared deep learning codes and Insar analysis results.
my general comments:
- The presentation of Insar monitoring, methodology, software and application, orientations, and so on is not acceptable and the paper needs to be improved significantly in this part.
- Why authors use the prepared codes for time-series assessment and what the innovation is in this section.
Short actions needed comments:
- Please use English figures (all figures)
- I detected some figures from different web pages and papers without citations in the manuscript
I look forward to receiving major changes in the manuscript and in detail answers to comments.
Best regards,
Author Response
The notes are in the attached files.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
INTERESTING PAPER, THE AUTHORS SHOULD IMPROVE THE FINAL COMMENTS AND THE CONCLUSIVES WITH LARGER COMPARISONS INHERENT THE RESULTS MADE WITH RESPECT TO THE SCIENTIFIC LITERATURE OF THE SECTOR
Author Response
The notes are in the attached files.
Author Response File: Author Response.pdf
Reviewer 3 Report
All comments have been resolved and it is recommended to accept in present form.
Author Response
The notes are in the attached files.
Author Response File: 
Author Response.docx
Reviewer 4 Report
Dear authors,
Thank you for submitting your amendment. My final decision is "Accept" in regard to this work.
Best regards,
Author Response
The notes are in the attached files.
Author Response File: Author Response.docx
