A Machine Learning-Driven Approach to Uncover the Influencing Factors Resulting in Soil Mass Displacement

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

General comments:

I have reviewed the manuscript entitled ‘A machine learning-driven approach to uncover the influencing factors resulting in soil mass displacement’.

This study takes a data-driven approach to explore correlations between various influencing factors triggering slope movement to perform dimensionality reduction, then feature selection and extraction to identify which measured factors have the strongest influence in predicting slope movements via a supervised regression approach. Further, through hierarchical clustering of the aforementioned selected features, it also identifies distinct types of displacement. By selecting the most effective measures, this in turn informs the subset of sensors needed for deployment on slopes prone to failure to predict imminent failures. I recommend a major revision is warranted. I explain my concerns on more detail below. I ask the authors specifically address each of my comments in their response.

 

1. In 4.3, What is the sample size for data selection? The authors compared the RF-LDA model with 70/30% and 50/50% training/testing splits. Why were these two partition methods chosen for comparison? What about other partition types, such as 80/20%?

 

2. Could you please explain the reasons for selecting these three models (Lasso, RF, XGBoost)? Additionally, how would you describe the efficiency, generalizability of these models?

 

3. In 4.4, the performance of RF and XGBoost feature selection differs in predicting displacement. Does this conclusion also apply to other regions?

 

4. In 2.1, the selected influencing factors are all natural. Could you please elaborate on how geological conditions and human activities were considered?

 

Minor comments:

5. In 1, The introduction section is too lengthy. I suggest that the authors condense it appropriately.

 

6. In Figure 5, the predicted cumulative displacement over different time windows (e.g., t=1d, 5d, 10d, 15d, 30d) is recorded. However, the text only mentions the time windows of t=5d, 10d, 15d, and 30d. Could there be an omission regarding the explanation for the predicted cumulative displacement at t=1d?

 

7. Although the effectiveness and applicability of the experimental results in Sections 4 and 5 have been analyzed in detail, I still suggest that the following content be added to the discussion section: There are differences in the performance of the three algorithms. Are they reliable for relative landslide displacement?

 

8. In Table 4, why were two indicators chosen instead of all four? Is there a reference basis for selecting these two indicators?

 

9. Please note that the images are missing units. It would be helpful to include them (e.g., in Figures 5 and 6).

 

10. Can the experimental scheme be integrated with remote sensing data for comprehensive analysis?

Comments on the Quality of English Language

English is very good, but slight editing of the English language is required

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript deals with A machine learning-driven approach to uncover the influencing factors resulting in soil mass displacement.

The work is interesting since it relates several variables affecting mass movement. It is necessary to clearly define what constitutes the Introduction, Materials and Methods, Results, Discussion, and conclusions.

 

Additionally, it would be good to incorporate more bibliographical citations in the introduction since it is very scarce and punctual.

It is necessary to insert a map of the location of the study area and add geological data of said area, as well as the lithology.

Equipment is used for measurements in the study area. To underline the thoroughness and comprehensiveness of your research, it is essential to incorporate images of each piece of equipment and its location in the study area.

In the results and discussion section, it is necessary to relate the data obtained with possible relationships established in other works. The conclusions are very general; it would be good to show the most relevant data of the study so that the reader can review the data in detail.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript investigates a predictive model for landslide soil displacement based on machine learning methods, thereby revealing the influencing factors of soil displacement. The topic is of positive significance for landslide disaster prevention and mitigation. However, there are some issues in the manuscript that require careful revision: 1. Firstly, the structure of the manuscript needs modification. It is recommended to change the structure to five sections: Introduction, Data and Methods, Results and Analysis, Discussion, and Conclusion. The current structure is not conducive to readability. 2. In the Introduction, it is not advisable to include too much specific information about the work, such as Figure 1. The abstract should summarize the deficiencies of previous studies to introduce the results of this manuscript, thereby increasing the logical coherence of the work. 3. Undoubtedly, specific information about the landslide is very important for readers. However, this manuscript does not provide any information about this landslide, especially figures. Therefore, I suggest the authors significantly supplement the information about this landslide, including geology, topography, remote sensing images, photos, etc. 4. The work presented in this manuscript is a special case. Whether the findings are applicable to other cases or whether the pattern is universally applicable should be discussed. 5. The discussion in this manuscript is relatively weak. It is recommended that the authors analyze the advantages and limitations of their work from multiple perspectives, in conjunction with previous research. They should analyze some boundary conditions and uncertainties of this work and provide prospects for future research. In summary, major revisions are recommended.

Comments on the Quality of English Language

no

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I am pleased to recommend the acceptance of this paper for publication.

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript has been revised according to expert opinions, resulting in a significant improvement in the quality of the manuscript. I think the current quality of the manuscript meets the requirements for acceptance, and ACCEPT is suggested.

Comments on the Quality of English Language

fine