Identification and Classification of Oil and Gas Pipeline Intru-sion Events Based on 1-D CNN Network

PLOS ONE

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Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: 1- The manuscript integrates both handcrafted features and raw DAS signals within a 1-D CNN. Could the authors clarify how feature-level fusion was implemented, and whether alternative fusion strategies were considered?

2 - The paper employs transfer learning from a human activity dataset to improve generalization. How do the authors ensure that the domain gap between human motion data and pipeline intrusion signals does not introduce bias or spurious correlations?

3- The dataset used for pipeline intrusion detection was collected under confidentiality agreements. Although a simulated dataset is provided, how representative is this dataset of real-world conditions, and what limitations might this impose for reproducibility and field deployment?

4- The experiments report high accuracy and robust classification performance. Could the authors expand on how their model handles class imbalance and whether performance is consistent across all intrusion categories, particularly rare events?

5- The architecture of the proposed 1-D CNN was optimized via empirical tuning and grid search. How sensitive are the results to hyperparameter choices, and have the authors explored automated methods such as Bayesian optimization or evolutionary search for architecture refinement?

6- The proposed approach demonstrates superior results compared to classical machine learning baselines. However, how does the method compare against more recent deep learning models, as LSTMs, Transformers, or hybrid CNN-RNN architectures, for time-series intrusion detection tasks?

Reviewer #2: 1.Why is a one-dimensional convolutional neural network adopted? Can other neural networks not be used? The paper should conduct a comparative analysis of the advantages and disadvantages of multiple methods.Some of the latest references are available for reference, such as DOI10.1016/j.jngse.2020.103716;10.1016/j.jngse.2021.104175

2.Pipeline systems are subjected to a multitude of intrusion events, such as excavation, impact, vehicle rollover, and human activities. The article only studies human activities; how to ensure the applicability of its method is a question that needs to be addressed.

3."Reyes-Ortiz, J., Anguita, D., Ghio, A., Oneto, L., & Parra, X. (2013). Human Activity Recognition

236 Using Smartphones [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C54S4K",How do smartphone-based datasets reflect the characteristics of pipeline intrusion events?

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Reviewer #1: No

Reviewer #2: No

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Identification and Classification of Oil and Gas Pipeline Intru-sion Events Based on 1-D CNN Network

PONE-D-25-41494R1

Dear Dr. Huang,

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.

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Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: (No Response)

Reviewer #2: The authors has made a sound response to the reviewer's comments, and I think it can be published .

Reviewer #3: Dear Authors,

Thank you for this detailed revision. The revised manuscript deserves the acceptance of publication.

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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 #1: No

Reviewer #2: No

Reviewer #3: Yes:  Ghassan Abdul-Majeed

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