Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis manuscript tackles an important and relevant issue: the prediction of coastal retreat in permafrost-dominated Arctic regions. The authors employ a range of statistical and machine learning methodologies to assess and predict shoreline dynamics, providing a detailed case study of a section of the Ural coast along the Baydaratskaya Bay in the Kara Sea. The work is timely and relevant, given the accelerating effects of climate change on Arctic coastal environments. However, I have the following specific comments and suggestions to improve the manuscript:
(1) In the introduction, it would be helpful to provide a more explicit comparison with previous studies that have used machine learning for similar environmental analyses. This would situate the study within the broader research context.
(2) The article mentions that the dataset is referenced in Citation 25, along with the types of data included. However, I would recommend that the authors provide a brief summary of the dataset's time span and other relevant details in a table or alternative format to enhance clarity.
(3) Include a clearer justification for choosing specific algorithms and parameters (e.g., why “OneHotEncoder” or why a particular window size for the median filter?).
(4) I recommend the authors include a side-by-side comparison of raw vs. processed data to visually emphasize the efficacy of the employed methods.
(5) Are the predictions robust across different test datasets, or is there overfitting to the study area?
Author Response
Dear reviewer,
Thank you for your valuable comments and suggestions. Your feedback has helped improve the quality of the article.
Comments 1: In the introduction, it would be helpful to provide a more explicit comparison with previous studies that have used machine learning for similar environmental analyses. This would situate the study within the broader research context.
Response 1: The introduction has been rewritten. Much of AI research focuses on processing raster images, but we analyze numerical data about coastal retreats in our work.
Comments 2: The article mentions that the dataset is referenced in Citation 25, along with the types of data included. However, I would recommend that the authors provide a brief summary of the dataset's time span and other relevant details in a table or alternative format to enhance clarity.
Response 2: Added table with metadata and observed time intervals in the text.
Comments 3: Include a clearer justification for choosing specific algorithms and parameters (e.g., why "OneHotEncoder" or why a particular window size for the median filter?).
Response 3: Upon obtaining the dataset, we experimented with various algorithms. This article discusses those that yielded the most interesting results. This is the first step in introducing a new approach to forecasting coastal dynamics, combining data analysis (statistical methods) with artificial intelligence. The 250-meter interval of the median filter is associated with the analysis of previously obtained variograms showing spatial correlations for coastal areas.
Comments 4: I recommend the authors include a side-by-side comparison of raw vs. processed data to visually emphasize the efficacy of the employed methods.
Response 4: We incorporated raw data into the application and utilized a neural network to process this data. The separation of noise and signal was performed directly on the raw, noisy data. Such row data are not suitable for data analysis and require preparation.
Comments 5: Are the predictions robust across different test datasets, or is there overfitting to the study area?
Response 5: A neural network was used to isolate noise in the data. This was the very first step, and we were faced with developing methods and forecasting approaches. As for factor analysis, we checked such an approach for another key site in the Kara Sea, and it also showed some patterns (there will be another article about this)
best regards,
Daria
Reviewer 2 Report
Comments and Suggestions for AuthorsIn this manuscript, the long-term characteristics of permafrost coastal erosion were investigated using data analysis. There is a useful contribution to researchers in this area, and I recommend the manuscript to be acceptable as it is, except for the future prediction. For the future prediction, the change in the weather conditions is assumed to be small and no severe events like storms are assumed to hit the coast. The reviewer thinks that such preconditions should be mentioned more clearly in the manuscript.
Author Response
Dear Reviewer,
Thanks for your comments and suggestions; correcting the article will improve it.
Comments 1: In this manuscript, the long-term characteristics of permafrost coastal erosion were investigated using data analysis. There is a useful contribution to researchers in this area, and I recommend the manuscript to be acceptable as it is, except for the future prediction. For the future prediction, the change in the weather conditions is assumed to be small and no severe events like storms are assumed to hit the coast. The Reviewer thinks that such preconditions should be mentioned more clearly in the manuscript.
Response 1: This study consists of two distinct yet interconnected parts. The first part focuses on data analysis and identifying significant patterns. Using factor analysis, including categorical data, helps to determine which natural coastal features most significantly influence coastal retreat and identify areas with the greatest risk of intense transformation. It is a forecast estimate at a qualitative level. The second part involves using a neural network to separate noisy data on retreat rates into signal and noise components.
What about weather conditions? We chose a short coastal segment that will be under the influence of identical meteorological conditions. This means that wind-wave energy and the thawed index will be the same for the entire section but different in different periods. Their changes will occur proportionally throughout the entire area under consideration. This approach allows us to simplify the analysis of coastal zone dynamics and consider general patterns of changes within the segment.
best wishes
Daria
Reviewer 3 Report
Comments and Suggestions for Authors
>> The research objectives are not clearly defined in the introduction. How does this study contribute to the existing knowledge on erosion prediction?
>> The connection between the problem statement and the methodology is unclear, especially how factor analysis and neural networks directly address coastal prediction.
>> Line 63: Existing enough data is not a justification to use a neural network; please explain in technical term
>> Line 63: "a lot of data" is imprecise and does not contribute to the model choice justification
>> Using inverse convolutional networks is innovative but poorly contextualized within the broader landscape of permafrost erosion studies.
>> Provide more context on the importance of predicting permafrost coast erosion and the current state of research in this field and clearly articulate the knowledge gaps they are attempting to fill.
>> The methodology section includes more details on the specific model architectures, hyperparameters, and training procedures to assess the robustness of the approach.
>> Section 3.3.2 lacks references as background for the proposed/used method
>> Please provide a graphical flowchart of the proposal.
>> Neuron net is not a common term in the ML field
>> Include the statistical significance of the identified relationships
>> The authors should discuss the proposal's strengths and limitations as well as compare it to other predictive modeling techniques used in this domain.
>> Introduction and conclusion sections repeat ideas without expanding on them.
>> What are the practical implications, and how could the proposed methodology be applied to support coastal management?
Comments on the Quality of English LanguageThe English could be improved to more clearly express the research.
Author Response
Dear Reviewer,
Thank you for your valuable comments and suggestions. Your feedback has helped improve the quality of the article.
Comments 1: The research objectives are not clearly defined in the introduction. How does this study contribute to the existing knowledge on erosion prediction?
Response 1: The introduction has been rewritten. We are discussing knowledge about coastal retreat in the Arctic (erosion in the title means ''destruction"). We have tried to outline many problems that currently exist when studying the coasts of the remote Arctic.
Comments 2: The connection between the problem statement and the methodology is unclear, especially how factor analysis and neural networks directly address coastal prediction.
Response 2: This was scientific research to develop methods and approaches to predicting coastal dynamics using new methods (date analysis and neural networks). Much of AI research focuses on processing raster images, but in our work, we employ neural networks to analyze numerical data about coastal retreats. It is the first step in this direction.
Comments 3: Line 63: Existing enough data is not a justification to use a neural network; please explain in technical term
Response 3: We have deleted it
Comments 4: Line 63: "A lot of data" is imprecise and does not contribute to the model choice justification
Response 4: We have deleted it.
Comments 5: Using inverse convolutional networks is innovative but poorly contextualized within the broader landscape of permafrost erosion studies.
Response 5: We use a neural network to separate noisy data on retreat rates into signal and noise components. It is better than the use of the median filter.
Comments 6: Provide more context on the importance of predicting permafrost coast erosion and the current state of research in this field and clearly articulate the knowledge gaps they are attempting to fill.
Response 6: The introduction has been rewritten. We have tried to outline many problems that currently exist when studying the coasts of the remote Arctic.
Comments 7: The methodology section includes more details on the specific model architectures, hyperparameters, and training procedures to assess the robustness of the approach.
Response 7: Upon obtaining the dataset, we experimented with various algorithms. This article discusses those that yielded the most interesting results. This is the first step in introducing an entirely new approach to forecasting coastal dynamics, combining data analysis (statistical methods) and artificial intelligence. This study consists of two distinct yet interconnected parts.
Comments 8: Section 3.3.2 lacks references as background for the proposed/used method
Response 8: This was a scientific research work to develop methods and approaches to predicting coastal dynamics using new methods. I didn't find this approach in scientific fields of arctic coastal dynamics. Much of AI research focuses on processing raster images, but in our work, we employ neural networks to analyze numerical data about coastal retreats. It is the first step in a new direction.
Comments 9: Please provide a graphical flowchart of the proposal.
Response 9:We don't understand. Do you mean the methodology scheme with the list of methods or the coast scheme with the retreat rate?
Comments 10: Neuron net is not a common term in the ML field
Response 10: we have changed to "neural network."
Comments 11: Include the statistical significance of the identified relationships
Response 11: Statistical significance included in correlation analysis as a p-value.
Comments 12: The authors should discuss the proposal's strengths and limitations as well as compare it to other predictive modeling techniques used in this domain.
Response 12: All approaches to predictive modeling of arctic coastal retreat are regional and focused on physical simulating (including a lot of parameters) the shoreline for a particular part of the selected coast. It limits the possibilities of developing universal forecast models. In this study, we try to develop new methods and approaches that can become universal.
Comments 13: Introduction and conclusion sections repeat ideas without expanding on them.
Response 13: The introduction and conclusion have been rewritten
Comments 14: What are the practical implications, and how could the proposed methodology be applied to support coastal management?
Response 14: Arctic shores are rarely associated with vacations and beach tourism. They often serve industrial purposes, such as transporting hydrocarbon resources from remote regions. The use of an approach involving factor analysis makes it possible to identify sections of the shoreline that will remain stable and those that are prone to changes. Access to these areas is extremely challenging, and research opportunities are limited to short seasonal windows. However, by utilizing remote sensing data and complementing it with fieldwork results obtained over a single season, it is possible to significantly expand our understanding of lithological and permafrost conditions and the development of natural processes over large areas. As a result of this approach, it becomes clear where construction can be safely carried out and where substantial investment in coastal protection structures will be necessary. This method reduces risks and optimizes the costs of developing Arctic territories.
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors made significant improvements in the manuscript.
However, is it worthy to clarify comment 9:
Concerning comment 9:
A flowchart (or computationa framework, or modeling procedure) for a proposal of an AI method is a visual representation that outlines the sequence of steps, processes, or decisions involved in implementing the proposed AI approach. It helps to clarify the methodology and provides a high-level overview of how the system operates.
Please check the following and include a figure describing the proposed approach.
- Figure 1 in https://www.mdpi.com/2227-7390/10/16/3019
- Figure 3 in https://www.mdpi.com/2227-7390/10/16/2971
- Figure 7 in https://www.mdpi.com/2227-7390/10/16/2825
Author Response
Dear Reviewer,
Thanks for your explanation.
Comments 1: Please check the following and include a figure describing the proposed approach.
Response 1: I added a picture with the procedures in our study (Figure 3)
Author Response File: 
Author Response.docx
