PONE-D-25-46623Enhancing rainfall estimation accuracy with machine learning, cloud masking, and multi-source data: a case study of four coastal provinces in central VietnamPLOS ONE

Dear Dr. Nguyen,

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PLOS ONE

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Additional Editor Comments:

Two reviewers have provided constructive comments to help the authors improve their manuscript. I agree with their suggestions, particularly the need to extend the study period and to conduct a more comprehensive review of the subject matter.

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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

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

Reviewer #1: No

Reviewer #2: Yes

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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: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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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: A. Article Overview

This manuscript describes a machine learning-based methodology designed to significantly improve rainfall estimation accuracy in the four coastal provinces of Central Vietnam, a region characterized by complex topography and climate. The researchers developed an innovative machine learning-based product using a Light Gradient Boosting Machine (LGBM) model fed with multi-source data, including Himawari-8 satellite imagery, ERA5 reanalysis data, ASTER DEM, and ground-based rain gauge measurements.

A key innovation is a three-stage classification architecture that first distinguishes rain from no-rain areas, then classifies rain into low or high intensity, and finally categorizes it into four detailed classes: small, moderate, heavy, and very heavy rain. This process is filtered by two cloud masks generated from brightness temperature thresholds to identify rain-bearing clouds and their intensity, alongside data balancing techniques to address severe class imbalance. The efficacy of the resulting outcome was evaluated against five established regional rainfall products and weather radar imagery.

B. Observations.

1. The model is trained on data from only two years (2019-2020), so from a statistical point of view the period is not relevant. A longer time series is needed, which includes more years with different climatic conditions.

2. The use of cloud masks, while reducing false alarms, also led to a decrease in the probability of detection, and with regard to extreme events the model cannot be considered relevant, an aspect also recognized by the authors.

3. The use of rain gauge data for supervised machine learning training makes the accuracy of the model good but is related to their resolution.

C. General comments

The research seems interesting but it needs to be redone on a statistically relevant series, and I do not necessarily require it to be a WMO reference period, for example 1991-2020, or a statistically valid one of at least 20 years, but it must contain at least 8 years of training, with two years of validation.

Best wishes,

The reviewer.

Reviewer #2: The manuscript presents an approach that integrates a Light Gradient Boosting Machine (LGBM) model with multi-source data to classify and estimate rainfall in four coastal regions of Central Vietnam. Although the topic is relevant and the study contributes to a broader effort to improve the accuracy of rainfall forecasting, the manuscript in its current form requires significant revision to meet standards.

The abstract, for example, contains an excessive amount of detail that obscures the main purpose and contribution of the study. It should be thoroughly revised to be more concise and focused, presenting only the essential elements: the research problem, objectives, a brief description of the proposed approach, the main results, and their significance. Overly detailed methodological explanations and lengthy background information should be removed to ensure that the abstract clearly conveys the essence and relevance of the study to a broad audience.

Similarly, the introduction does not adequately place the study in the context of existing research. It lacks a critical synthesis of previous studies and does not clearly articulate the specific knowledge gaps that the work seeks to address. To strengthen this section, the author should integrate the relevant literature more effectively, highlight the limitations of previous work, and explicitly explain how this study builds on and advances previous efforts. A more thorough and focused literature review would make the novelty and research needs clearer to the reader.

The methodology section also needs significant reorganization. Its current structure is fragmented, and its logical sequence is difficult to follow. Authors are encouraged to begin by outlining the overall methodological framework and data processing steps before explaining the theoretical and algorithmic details of the LightGBM model. Establishing a clearer structure will help the reader understand the study design and the rationale for the chosen approach.

A further limitation is the exclusive reliance on the LightGBM model without comparison with other robust machine learning or statistical models. Without such a benchmark, it is difficult to assess the relative performance or originality of the proposed method. Incorporating additional underlying models such as Random Forest, XGBoost, Support Vector Machines, or appropriate deep learning models would provide a stronger basis for evaluating the effectiveness and specificity of the approach.

In addition, the manuscript does not describe any model optimization or hyperparameter tuning strategies. Since hyperparameter tuning is essential to achieve robust and reproducible performance, authors should clearly state the optimization method used (such as grid search, random search, Bayesian optimization or Optuna) and report the tuned parameters used in the final model. Providing this information is essential to ensure transparency, reproducibility and fairness in model evaluation.

The discussion section would also benefit from a stronger engagement with prior research. It currently lacks critical comparisons with existing studies, which weakens the findings of this study. Incorporating relevant prior findings and discussing how the results align with or deviate from them would help contextualize the study’s contribution and explain how it advances current knowledge in the field.

Finally, the conclusion section should be revised for brevity and focus. It should not repeat detailed findings but instead synthesize the main contributions, outline their implications, and briefly note directions for future work. Although the manuscript addresses an important and timely topic, it requires substantial revision to improve structure and clarity before it can be considered for publication.

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Reviewer #1: Yes: Cornel Ilinca

Reviewer #2: No

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PONE-D-25-46623R1Enhancing rainfall estimation accuracy with machine learning, cloud masking, and multi-source data: a case study of four coastal provinces in central VietnamPLOS One

Dear Dr. Nguyen,

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 Mar 04 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,

Mou Leong Tan

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.

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Additional Editor Comments:

The authors have satisfactorily addressed most of the reviewers' concerns. However, a few minor points still require attention. I recommend a final round of revisions. Thank you.

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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 #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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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 #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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 #1: Yes

Reviewer #2: Yes

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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 #1: Yes

Reviewer #2: Yes

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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 #1: The authors' response to Point 1 is correct. They successfully demonstrated that the model was not overfitted to the initial two-year period, as it maintains consistent performance over the extended five-year dataset.

Regarding Point 2, the potential lack of relevance for extreme events reflects a deliberate methodological choice to optimize global error. However, it should be noted that high-intensity, rare, or localized events could still substantially influence model outcomes.

Regarding Point 3, the map provided confirms that the rain gauge stations offer uniform territorial coverage, ensuring the spatial representativeness and validity of the model's training.

Reviewer #2: The authors have addressed the reviewers’ comments and made the necessary corrections. However, the manuscript should be carefully proofread prior to publication, and the quality of the figures should be further improved to enhance clarity and readability.

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Reviewer #1: Yes: Cornel Ilinca

Reviewer #2: No

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Enhancing rainfall estimation accuracy with machine learning, cloud masking, and multi-source data: a case study of four coastal provinces in central Vietnam

PONE-D-25-46623R2

Dear Dr. Nguyen,

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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Kind regards,

Mou Leong Tan

Academic Editor

PLOS One

Additional Editor Comments (optional):

Most reviewer comments have been addressed, and the manuscript is now suitable for publication in PLOS ONE. The study provides valuable benefits for rainfall estimation in Vietnam.

Reviewers' comments: