Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model

Yongxin Dai; Zijian Li; Jingbiao Lu

doi:10.1371/journal.pone.0323650

Peer Review History

Original SubmissionDecember 12, 2024
12 Dec 2024 Author Response https://doi.org/10.1371/journal.pone.0323650.r001
14 Jan 2025 Decision Letter - Linwei Li, Editor Dear Dr. Li, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== ACADEMIC EDITOR: The review process is now complete, and we have two reports submitted by the expert reviewers. As can be seen from the reports, both reviewers request major revisions before it is reviewed again. In particular, reviewers #1 and #2 proposed many critical comments on applying technical terms, research summary, research innovations, model comparisons, and research depths in this study. Thus, I invite you to revise the submitted manuscript carefully. Meanwhile, I want to thank the reviewers for their great efforts on your manuscript and you for your submission. 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Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: No Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: No Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: No Reviewer #2: Yes ****** Reviewer #1: 1. The title of the paper 'A Novel Method... Hybrid Data Driven Model' is too vague. Suggest modifying it to a specific method statement. 2. The authors may have confused the concept and difference of the specialized term “hazard” and “risk”. From the perspective of the main content, this work only involves “hazard”. Please refer to previous publications and follow the landslide risk assessment framework. For example: van Westen et al., 2006, https://doi.org/10.1007/s10064-005-0023-0, Wang et al., 2021, https://doi.org/10.3390/rs13132625�Wen et al., DOI: 10.1016/j.jenvman.2023.118177. Suggest modifying the relevant terms throughout the context, such as: the "risk" should be "hazard", the "Early warning" should be "Early hazard warning". 3. The Introduction section of the manuscript does not clearly state the research gap, nor does it highlight the innovation and contribution of this work. Suggest adding the state-of-the-art literatures, such as: Doi: 10.1016/j.gsf.2024.101959, Doi: 10.1016/j.gr.2023.09.016, Doi: 10.1007/s11440-023-02050-9 to further clarify the research gap. 4. For displacement prediction models with periodic and trend terms, it should be further clarified how to divide the training and testing datasets. 5. The author should further clarify the details of the study case, the rock slope and the monitoring data (displacement). 6. What are the limitations of this work comparing relevant previous publications? Need to do in-depth analysis in the discussion section. 7. There is no unified for the specialized terms. As shown in the legend in the upper right corner of Figure 10, "Cycle" may be "periodic". It should review the entire context and further polish the writing English. Reviewer #2: The manuscript presents a hybrid approach to landslide early warning with significant potential for practical application. By implementing the following suggested revisions, the paper can be strengthened methodologically and provide more comprehensive insights into slope stability prediction. 1. While the paper compares four models, consider adding more machine learning models or ensemble techniques for a more comprehensive comparison. 2. Elaborate on the limitations of the sliding average-wavelet noise reduction method and potential alternative approaches. 3. Provide a more in-depth theoretical explanation of how different environmental factors interact to influence slope displacement. 4. Include a section discussing the interpretability of the LSTM-SARIMA hybrid model, explaining how it captures complex nonlinear relationships. 5. Enhance the manuscript with more detailed visualizations, particularly showing the intermediate steps of data processing and model prediction. 6. Develop a more comprehensive section explicitly discussing the study's limitations and potential sources of error. 7. Furthermore, the work would greatly benefit from including and referencing more recent material on practical implementations of machine learning methods across several domains. It is recommended to engage in discussions on the comparison of results and the integration of these concepts into your work. You may find more information on this topic in the following articles: https://doi.org/10.1007/s11004-023-10116-3, https://doi.org/10.1007/s11069-024-06917-2, https://doi.org/10.1007/s12583-021-1525-9, https://doi.org/10.1007/s12583-021-1407-1. 8. “Malery et al. (Malamud et al., 2010) Li Tianbin et al. (Chen et al., 2016)”: List only the first author's name followed by “et al.” in every citation. 9. More details of the open-pit mine slope should be provided. Picture and cross-section should be included. 10. Provide more details about the data collection process, including the specific radar monitoring equipment used, installed location, its precision, and calibration methods. 11. Fig. 16: please include the landslide boundary. 12. Conduct a thorough proofreading of the manuscript to correct any grammatical errors, improve sentence structure, and enhance overall clarity. ****** what does this mean? ). If published, this will include your full peer review and any attached files. 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Revision 1
20 Mar 2025 Author Response To Reviewer 1: 1. The title of the paper 'A Novel Method... Hybrid Data Driven Model' is too vague. Suggest modifying it to a specific method statement. �Response We thank the experts for their valuable comments and we apologize for the unclear presentation of the title of the paper. In this paper, we mainly predict the slope displacement by LSTM-SARIMA hybrid data-driven model, which is combined with the early warning criterion of improving the tangential angle to complete the accurate early warning of slope landslide disaster. The comments made by the experts are very valuable, therefore, we modify the title of the paper to “Landslide Hazard Early Warning System for Rock Slopes Using a Hybrid LSTM-SARIMA Data-Driven Model .” 2.The authors may have confused the concept and difference of the specialized term “hazard” and “risk”. From the perspective of the main content, this work only involves “hazard”. Please refer to previous publications and follow the landslide risk assessment framework. For example: van Westen et al., 2006, https://doi.org/10.1007/s10064-005-0023-0, Wang et al., 2021, https://doi.org/10.3390/rs13132625�Wen et al., DOI: 10.1016/j.jenvman.2023.118177. Suggest modifying the relevant terms throughout the context, such as: the "risk" should be "hazard", the "Early warning" should be "Early hazard warning". �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. In this paper, through the technical processes of data noise reduction, displacement component separation, slope displacement prediction, and early warning indicator establishment, we achieve accurate early warning of slope disasters. We are sorry that the expression of risk and hazard is not clear enough here, thanks to the valuable opinions of the experts, we have made corresponding modifications in many places in the paper to change the risk to hazard. 3.The Introduction section of the manuscript does not clearly state the research gap, nor does it highlight the innovation and contribution of this work. Suggest adding the state-of-the-art literatures, such as: Doi: 10.1016/j.gsf.2024.101959, Doi: 10.1016/j.gr.2023.09.016, Doi: 10.1007/s11440-023-02050-9 to further clarify the research gap. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. The introductory part of this paper focuses on the research done by experts and scholars on slope displacement prediction in the past time, mainly focusing on both mathematical methods and artificial intelligence algorithms. For the research results of scholars in recent years are lacking, therefore, this paper adds the results taken by scholars in this direction in recent years. 4.For displacement prediction models with periodic and trend terms, it should be further clarified how to divide the training and testing datasets. �Response Thank you very much for this expert's valuable opinion, this opinion is of great value to improve the quality of our article. This paper lacks elaboration on dataset partitioning, therefore, we will explicitly state how the training and testing sets are partitioned in the paper. 5.The author should further clarify the details of the study case, the rock slope and the monitoring data (displacement). �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. Further details of the study cases, rocky slopes and monitoring data will be elucidated in the text. 6.What are the limitations of this work comparing relevant previous publications? Need to do in-depth analysis in the discussion section. �Response Many thanks to the experts for their valuable inputs. This comment is very helpful in improving the quality of the article. After describing the theory, methodology, and application, this article lacks discussion and comparison of research results. We will add a discussion section at the end of the article to clarify the effects and limitations of the research content. 7.There is no unified for the specialized terms. As shown in the legend in the upper right corner of Figure 10, "Cycle" may be "periodic". It should review the entire context and further polish the writing English. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. We will standardize terminology throughout the text. To Reviewer 2: The manuscript presents a hybrid approach to landslide early warning with significant potential for practical application. By implementing the following suggested revisions, the paper can be strengthened methodologically and provide more comprehensive insights into slope stability prediction. 1.While the paper compares four models, consider adding more machine learning models or ensemble techniques for a more comprehensive comparison. �Response Thanks a lot to the experts for their valuable comments, this comment is extremely helpful in improving the quality of the article. In this paper, three machine learning models are used to do performance comparison with hybrid LSTM-SARIMA model. Based on the comments made by the experts, we added RNN and LightGBM, which are very widely used nowadays, for displacement prediction. 2.Elaborate on the limitations of the sliding average-wavelet noise reduction method and potential alternative approaches. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation.We will detail the limitations and possible alternatives to the sliding average wavelet noise reduction method in this paper. 3.Provide a more in-depth theoretical explanation of how different environmental factors interact to influence slope displacement. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. The mechanism of the effect of rainfall, groundwater level, and groundwater infiltration pressure on slope displacement is complex but well documented. This paper is not clear enough about how different environmental factors interact to affect slope displacement, so we have rethought and reorganized our work in order to express the mechanism more clearly. 4.Include a section discussing the interpretability of the LSTM-SARIMA hybrid model, explaining how it captures complex nonlinear relationships. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. Therefore, we have added a subsection to the text dedicated to the section on the interpretability of the LSTM-SARIMA hybrid model, explaining how it captures complex nonlinear relationships. 5.Enhance the manuscript with more detailed visualizations, particularly showing the intermediate steps of data processing and model prediction. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. We fully understand the importance of presenting the intermediate steps of data processing and model prediction in detail, however, the inherent nature of machine learning models (especially deep learning architectures), whose internal feature representations are often automatically generated through multiple layers of nonlinear transformations, makes it notably challenging to visualize the intermediate processes. Unlike white-box models, the feature extraction paths of such models are difficult to decouple into interpretable discrete steps, and their prediction mechanisms rely more on distributed representations than on manually predefined logical rules. In order to enhance the transparency of the methodology used in this paper, we provide relatively detailed descriptions of the mechanism, characteristics and effects of the models in Articles 2.2.1, 2.2.2 and 2.2.3, and make use of Fig. 3, Fig. 4 and Fig. 5 to provide an obvious demonstration of the computational process of various models. In practical application, we utilize Fig. 9 to demonstrate the comparison of model effects before and after noise reduction. We also demonstrate the results of model prediction using Fig. 10 to Fig. 11, and plot the error line graph (Fig. 14) to show the performance of different machine learning models. Once again, we thank the experts for their valuable comments. We are aware that the current visualization presentation is still lacking. However, the characteristics of the model prevented us from proceeding further. We kindly request the experts for their understanding. 6.Develop a more comprehensive section explicitly discussing the study's limitations and potential sources of error. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. We will explicitly discuss the limitations of the study and potential sources of error based on a comparison of existing research results and the findings of this paper. 7.Furthermore, the work would greatly benefit from including and referencing more recent material on practical implementations of machine learning methods across several domains. It is recommended to engage in discussions on the comparison of results and the integration of these concepts into your work. You may find more information on this topic in the following articles: https://doi.org/10.1007/s11004-023-10116-3, https://doi.org/10.1007/s11069-024-06917-2, https://doi.org/10.1007/s12583-021-1525-9, https://doi.org/10.1007/s12583-021-1407-1. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. The introductory part of this paper focuses on the research done by experts and scholars on slope displacement prediction in the past time, mainly focusing on both mathematical methods and artificial intelligence algorithms. For the research results of scholars in recent years are lacking, therefore, this paper adds the results taken by scholars in this direction in recent years. 8.“Malery et al. (Malamud et al., 2010) Li Tianbin et al. (Chen et al., 2016)”: List only the first author's name followed by “et al.” in every citation. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation.We have changed the corresponding expression in the text. 9.More details of the open-pit mine slope should be provided. Picture and cross-section should be included. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation. Further details of the study cases, rocky slopes and monitoring data will be elucidated in the text. 10.Provide more details about the data collection process, including the specific radar monitoring equipment used, installed location, its precision, and calibration methods. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation.We will add to the text an elaboration on data acquisition, including the specific radar monitoring equipment used, installation locations, accuracy, and calibration methods. 11.Fig. 16: please include the landslide boundary. �Response Thanks for the Reviewers’ careful review. We are sorry for the paper's unclear presentation.We will show the landslide boundary. 12. Conduct a thorough proofreading of the manuscript to correct any grammatical errors, improve sentence structure, and enhance overall clarity. �Response Thanks for the Reviewers’ careful review. We apologize for the grammatical errors in the article. We will be checking and revising the entire article to correct all grammatical errors, improve sentence structure, and improve overall clarity. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0323650.r003
13 Apr 2025 Decision Letter - Linwei Li, Editor Landslide Hazard Early Warning Method for Rock Slopes Using a Hybrid LSTM-SARIMA Data-Driven Model PONE-D-24-57504R1 Dear Dr. Li, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Linwei Li Academic Editor PLOS ONE Additional Editor Comments (optional): None Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #2: Yes ****** Reviewer #2: (No Response) ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0323650.r004
Formally Accepted
Acceptance Letter - Linwei Li, Editor PONE-D-24-57504R1 PLOS ONE Dear Dr. Li, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Linwei Li Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0323650.r005

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