-->PONE-D-25-16551-->-->Comparison of scenario reduction approaches for reservoir inflow timeseries generated by a Bayesian Neural Network-->-->PLOS ONE

Dear Dr. Koo,

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.

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Academic Editor

PLOS ONE

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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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: Rewrite the abstract properly by following structure: Background, Objective, Methods, Results, Conclusion.

Avoid lengthy descriptions like "we evaluate the applicability of the four distance measures widely used in the literature"

Rewrite the objectives separately and clearly

Focus more on gaps in hydrological scenario reduction.

Add a clear novelty statement like "To the best of our knowledge, ...

Clarify the hyperparameter optimization process. Don't just say TPE is used

No proper discussion on missing data handling or data cleaning steps

Merge figures or reduce redundancy

In Discussion,

There is no critical comparison with past scenario reduction studies

Discuss more about limitations of BNN for peak inflow predictions

Add clear recommendation for practitioners on when to use which distance measure.

Rewrite long sentences or maybe Split into two clear sentences.

Read these studies to further enrich your study. (DOI: 10.1016/j.asoc.2023.110722) Provides a soft computing approach for hydrological prediction which can complement BNN in uncertainty modeling. (DOI: 10.1007/s11269-025-04142-5) Demonstrates different ML models for water prediction, useful to compare or benchmark scenario generation models. (DOI: 10.2166/wpt.2024.147) Discusses ML-based hydrological modeling that can supplement your scenario generation techniques. (DOI: 10.5772/intechopen.1006491) Discusses novel fuzzy-based models that can be integrated or compared with BNN-based scenario reduction.

These studies are suggested for you to check the content and improve your paper. IF THEY ARE APPROPRIATE ONLY.

Reviewer #2: To further strengthen the manuscript, we suggest a few improvements. First, while the authors have described the data sources and provided processed datasets, a more detailed explanation of the data preprocessing steps—such as how missing values and anomalies were handled—would enhance the reproducibility and transparency of the study. Second, although the comparison of scenario reduction methods is comprehensive, the current setup associates each distance metric with a different reduction algorithm (e.g., clustering for Manhattan and Euclidean distances, forward selection for Wasserstein and energy distances). To isolate the pure effect of the distance metrics, it would be valuable to apply a uniform reduction algorithm across all metrics. Third, while the study clearly recommends practical distance measures considering computational constraints, it could further benefit practitioners by providing specific operational guidelines, such as recommended numbers of reduced scenarios under given time limits. Overall, these additions would make the already strong manuscript even more robust and practically impactful.

**********

-->6. PLOS authors have the option to publish the peer review history of their article (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.

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Reviewer #1: No

Reviewer #2: No

**********

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-->PONE-D-25-16551R1-->-->Comparison of scenario reduction approaches for reservoir inflow timeseries generated by a Bayesian Neural Network-->-->PLOS ONE

Dear Dr. Koo,

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.

Please submit your revised manuscript by Aug 23 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Babak Mohammadi

Academic Editor

PLOS ONE

Additional Editor Comments:

Three reviewers evaluated this manuscript. Please consider their comments carefully during the revision process. Additionally, take into account the following points:

1. Add a flowchart in the methodology section to illustrate the workflow of the current study.

2. Ensure that each table and figure is clearly explained and discussed in the main text. The values presented in the tables should be mentioned and discussed in the text. Figures also require more detailed discussion in the text.

Comments from PLOS Editorial Office: We note that one or more reviewers has recommended that you cite specific previously published works during the current round and in an earlier round of revision. As always, we recommend that you please review and evaluate the requested works to determine whether they are relevant and should be cited. It is not a requirement to cite these works and you may remove them before the manuscript proceeds to publication. We appreciate your attention to this request.

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #2: I Don't Know

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #2: Although the contribution is significant, the evidence supporting the claims is weak and the conclusions are currently unconvincing, so more substantial revisions are needed.

1. There have been no cases in which the generated and reduced scenarios have been applied to actual stochastic optimal control, and their actual effectiveness has not been fully proven.

For this reason, it is unclear whether the proposed method will directly lead to improved operational performance. Related papers must be cited to make claims.

2. The accuracy of peak prediction in real data is clearly low, raising doubts about the reliability of BNN in flood response. This is due not only to the limitations of the model but also to bias in the training data, so data selection and pretreatment must be reconsidered.

Reviewer #3: All changes are included in the manuscript...........................................................

Reviewer #4: This study focuses on generating reservoir inflow scenarios using a Bayesian Neural Network and evaluating methods for reducing these scenarios. This topic is important for practical water resources applications, particularly in reservoir flood control optimization. The revised manuscript is meaningful and contributes valuable insights to the field, with results that adequately support its intended contribution. The paper can be recommended for publication after the following revisions:

1. The Abstract is too long and includes excessive technical detail that would be more appropriate in the main text. There is redundancy, particularly around scenario generation and reduction concepts, which are repeated several times. Consider reducing redundancy and shortening it to under 300 words for clarity and conciseness.

2. The manuscript uses “NSC” as an abbreviation for Nash-Sutcliffe Efficiency, which is not standard. Please use the widely accepted abbreviation “NSE” for clarity and consistency with the literature. Consider citing some updated papers and incorporating the performance metrics discussed in these studies to strengthen the evaluation of model performance.

3. The spelling and capitalization of method names and their abbreviations should be carefully reviewed to ensure consistency and alignment with common literature standards. Abbreviations should be defined in full at their first occurrence, followed by the abbreviation in parentheses. Subsequent mentions should use only the abbreviation.

4. The manuscript, especially the Abstract, frequently uses the active voice (e.g., “we develop,” “we evaluate”). It is generally recommended to use the passive voice to improve objectivity and better align with conventional academic style.

5. The Introduction lacks a clear, concise statement of the study’s novelty and specific contribution within reservoir inflow scenario generation and reduction.

6. The literature review is too detailed and occasionally unfocused. Consider condensing the literature review by focusing on the most relevant studies and directly linking them to the research gap addressed in this work. Consider including recent studies on sustainable flood risk management to better connect the practical relevance of inflow uncertainty with broader flood management frameworks.

7. Add a flowchart in the Method section to visually summarize the proposed scenario generation and reduction steps for clearer understanding.

8. Some sentences throughout the manuscript are overly long or contain multiple ideas, which affect clarity and flow. It is recommended to simplify these sentences by breaking them into shorter, more focused statements.

9. Consider briefly comparing the findings with similar recent studies to better highlight the methodological contributions and contextual novelty of this work.

10. The Conclusion should briefly and impactfully summarize the key findings. The study’s limitations are not sufficiently addressed. More explicit methodological recommendations for future research should be provided.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (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.

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Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

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-->PONE-D-25-16551R2-->-->Comparison of scenario reduction approaches for reservoir inflow timeseries generated by a Bayesian Neural Network-->-->PLOS ONE

Dear Dr. Koo,

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.

Please submit your revised manuscript by Jan 03 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

-->If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Dr. Anurag Barthwal, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Authors,

Based on the Reviewer’s evaluations, the manuscript requires significant revisions before it can proceed further in the review process. Please address all reviewer comments carefully and submit a revised version of the manuscript along with a detailed point-by-point response explaining how each comment has been handled.

Kindly ensure that the revised manuscript highlights all changes clearly (using colored text) to facilitate further assessment.

(Academic Editor)

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #4: (No Response)

Reviewer #5: Partly

**********

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

Reviewer #4: (No Response)

Reviewer #5: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #4: (No Response)

Reviewer #5: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #4: Yes

Reviewer #5: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #4: Thank you for addressing all my previous comments clearly and convincingly. Some aspects could be further developed in future studies, but overall, the revised manuscript provides a valuable contribution to the field.

Reviewer #5: The work provides a systematic way to generate and reduce probabilistic inflow scenarios using machine learning and quantitative metrics, helping reservoir managers make better flood-control decisions under uncertainty.

The manuscript is technically rich and addresses a relevant hydrological challenge. However, I have following observations about the work:

1. The introduction contains extensive citations and detailed explanations of prior work, making it dense and difficult to follow. It can be streamlined without losing scientific depth.

2. Contribution 1 (BNN-based scenario generation) is presented as novel, but such approaches already exist in hydrology and ML; the novelty is more incremental than claimed. Pleas clarify.

3. There is no performance comparison with other deep learning models (such as LSTM, GRU, CNN), which weakens the justification for BNN. Authors are suggested to provide the performance comparison with other deep learning models.

4. The TPE algorithm is explained in textbook detail, which disrupts the methodological flow and adds complexity that could be summarized briefly.

5. The use of random validation sampling for time-series prediction is problematic. Temporal data requires chronological splitting to avoid information leakage.

6. Although k-means, k-median, and forward selection are used, the manuscript lacks a strong justification for using these specific algorithms over more modern or optimal alternatives.

7. Only 9 flood events exist in the dataset. This large imbalance between normal and extreme flows severely limits peak-performance learning, yet the issue is not addressed with corrective techniques (e.g., resampling, extreme-event augmentation).

8. The model underpredicts flood peaks—a major practical limitation for reservoir flood control—but no strategy to mitigate this is proposed.

9. Since the model is probabilistic, metrics such as CRPS, prediction interval coverage, or quantile errors should be included.

10. The envelope size used to evaluate extreme-event preservation is intuitive but not widely established in literature; more justification is needed.

11. Correlation matrices are compared visually, but deeper statistical interpretation (e.g., lag-dependence preservation) is missing.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (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 #4: No

Reviewer #5: Yes: Dr. Nikhil Kumar

**********

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

-->

-->PONE-D-25-16551R3-->-->Comparison of scenario reduction approaches for reservoir inflow timeseries generated by a Bayesian Neural Network-->-->PLOS One

Dear Dr. Koo,

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.

Please submit your revised manuscript by Apr 30 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

-->If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ziqiang Zeng, Ph.D.

Academic Editor

PLOS One

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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Reviewer #4: All comments have been addressed

Reviewer #6: (No Response)

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Reviewer #4: (No Response)

Reviewer #6: Yes

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Reviewer #4: (No Response)

Reviewer #6: Yes

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Reviewer #4: (No Response)

Reviewer #6: Yes

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Reviewer #4: Yes

Reviewer #6: Yes

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-->6. Review Comments to the Author

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Reviewer #4: I thank the authors for addressing all previous comments. I have no further suggestions. I consider the manuscript suitable for publication in its current form.

Reviewer #6: The paper addresses a relevant and timely problem in stochastic reservoir flood control: how to select a compact set of inflow scenarios that preserves both statistical fidelity and extreme-event coverage while remaining computationally feasible for real-time operation. The methodology is well-grounded, the evaluation criteria are appropriate, and the results are thoroughly discussed. The following minor issues should be addressed before the paper is accepted for publication.

1) Line 5: "metrological uncertainties" should read "meteorological uncertainties." Metrology is the science of measurement, whereas meteorology is the science of weather and climate. Please correct this typo.

2) Lines 98-103: The section references in the closing paragraph of the Introduction are missing. The sentences "Section introduces the methodologies..." and "Section details the case study area..." lack their corresponding section numbers. Please correct.

3) Line 104: The acronym "MC" is used in the subsection heading before it is defined. It is only defined later, in line 124. Please define the acronym before its first use, or spell it out in the heading ("Monte Carlo dropout BNN").

4) Line 173: The definition of μᵢ in Eq 3 is inconsistent with the preceding text. Lines 160–162 correctly state that the k-median centroid is the element-wise median, whereas the k-means centroid is the element-wise mean. However, the caption of Eq 3 defines μᵢ as "the mean of all scenarios in the cluster Cᵢ" for both cases. For l = 1 (Manhattan distance, k-median), μᵢ should be the element-wise median, not the mean. Please correct the definition to distinguish the two cases, e.g., "the centroid of cluster Cᵢ (element-wise median for l = 1, element-wise mean for l = 2)."

5) Fig. 2: The URLs provided in the caption (www.data.or.kr and https://www.vworld.kr) could not be accessed. Please verify that these addresses are correct.

6) Lines 261-274: The training period begins in September 2012, while both test events precede this date (Event 1: August 2011, Event 2: August 2012). The rationale for starting training in September 2012 is not stated. Since data are reportedly available from 2011, a brief explanation of this choice would help readers understand whether this is a deliberate design decision (e.g., to ensure the test events are excluded from training) and its practical implications (i.e., the model is trained on data collected after the test events it is evaluated on).

7) Line 291: "when validation loss shows no improvement over a specified epochs" is grammatically incorrect. It should read "over a specified number of epochs."

8) Line 375: The size of the original scenario set is fixed at n = 1000. This choice is not justified. A brief explanation of the rationale—whether based on convergence of the empirical distribution, a pilot sensitivity study, or computational feasibility—would strengthen the methodology.

9) Line 498: A period is missing between "in the prediction horizon" and "Table 3." The two sentences run together without punctuation. Please insert a period after "prediction horizon."

10) Lines 510-511: The claim that "the computational time increases linearly as the number of reduced scenarios... increases" is inconsistent with both the Abstract and Table 4. The Abstract correctly states that the energy distance is "quadratic in m." Table 4 confirms this: when m increases fivefold (from 10 to 50), the energy distance computation time increases approximately 24.6-fold (from 72.5 to 1,787.3 s), which is consistent with quadratic scaling, not linear. Please correct line 511 to be consistent with the Abstract and the empirical data in Table 4.

11) Lines 521-646 (Discussion and Conclusions): Several minor language issues were found in these sections:

- Line 572: Figures should be cited in numerical order: "Figs 8 and 9" instead of "Fig 9 and 8."

- Line 606: "the Manhattan and Euclidean distances outperform in retaining" is missing a comparator. Please rephrase, e.g., "outperform the other measures in retaining."

- Line 613: "can not" should be written as one word: "cannot."

- Line 628: "other application" should read "other applications."

- Lines 634-635: "scales much less favourably computationally with the number of scenarios in the reduced set and as such less viable for time-sensitive operations" lacks a conjugated verb for the second predicate. Please rephrase, e.g., "... and is therefore less viable for time-sensitive operations."

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Reviewer #4: No

Reviewer #6: No

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Comparison of scenario reduction approaches for reservoir inflow timeseries generated by a Bayesian Neural Network

PONE-D-25-16551R4

Dear Dr. Ja-Ho Koo,

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