-->PONE-D-24-40156-->-->Forecasting Monthly Runoff in a Glacierized Catchment: A Comparison of Extreme Gradient Boosting (XGBoost) and Deep Learning Models.-->-->PLOS ONE

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Reviewers' comments:

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-->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

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: In this work, the Extreme Gradient Boosting (XGBoost), random forest (RF) and long-short term memory (LSTM) model was used to forecast monthly runoff for one month ahead of time in the Lotschental watershed in Switzerland. The work is well organized. however, with the following minor corrections, it is acceptable:

SPECIFIC RECOMMENDATIONS

ABSTRACT

• The abstract could briefly explain why these methods were chosen or how they contribute to the research.

• The results could be more specific, including quantitative outcomes or more detailed

• At the end of the Abstract, it is necessary to point out the global importance of this study for society. This would arouse greater interest from Journal readers.

INTRODUCTION SECTION

• The introduction effectively covers the necessary elements but could be improved by providing a more detailed summary of methods, emphasizing the scientific gap, and enhancing the problem statement with a broader context.

• If possible, reinforce the introduction with up-to-date references (2022-2024) below. Because it is important to include the most recent articles.

1. Improving the accuracy of rainfall-runoff relationship estimation using signal processing techniques, bio-inspired swarm intelligence and artificial intelligence algorithms

2. Monthly streamflow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform

3. Improving the accuracy of rainfall-runoff relationship estimation using signal processing techniques, bio-inspired swarm intelligence and artificial intelligence algorithms

4. Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows

5. Simulation of the potential impacts of projected climate and land use change on runoff under CMIP6 scenarios

MATERIALS & METHOD

METHODOLOGY

• Consider adding brief explanations on why specific methods were chosen.

RESULTS

• Improve the logical flow and ensure that each finding is clearly explained.

• Please give applied model parameters.

CONCLUSIONS

• Clearly stating the most significant findings, with specific reference to the data.

• Offer more detailed and actionable recommendations based on the study's results.

• Highlight the study's contribution to existing research and its potential implications for theory and practice.

• Ensure that each paragraph is concise and directly contributes to the discussion.

Reviewer #2: This paper proposes Extreme Gradient Boosting (XGBoost) and Deep Learning Models to predict the Monthly Runoff in a Glacierized Catchment. However, the quality of the paper should be improved a bit. Specific comments are as follows.

1. The graphical abstract/flow diagram is missing, it must be added.

2. The novelty statement and research gap should be highlights in innovative aspect of this study, specifically in abstract and Introduction.

3. The shortcomings of existing research should be summarized, and the innovation of this paper should be highlighted.

4. As described in the paper, Extreme Gradient Boosting (XGBoost) and Deep Learning Models has been widely applied in the various fields, such as river stage forecasting (https://doi.org/10.1007/s13201-024-02103-8; https://doi.org/10.1016/j.heliyon.2023.e16290), Drought Forecasting (https://doi.org/10.3390/w15040765) and long-term rainfall prediction (https://doi.org/10.1007/s00024-022-03189-4), Pan Evaporation (https://doi.org/10.1007/s00024-023-03426-4; https://doi.org/10.1007/s13201-022-01846-6). However, several aspects in health monitoring, such as the reason for the adoption of Extreme Gradient Boosting (XGBoost) and Deep Learning Models in this paper is not clearly explained, it need to be appropriately supplemented.

5. The algorithms of machine learning models should be simplified, and the innovation of this paper should be highlighted. Readers are more concerned about the advantages of the deep learning model proposed in this study in forecasting.

6. Line no 62, " Several studies [11–13]" the citation must be at the end of sentence.

7. Lin No 79, the referencing and citation must be according to the journal style. for instant, water table [28] and river stages (Choi et al., 2020), both type of citations are there. it should be check throughout the manuscript.

8. Due to the extensive use of abbreviations, it is essential to include a list of acronyms for clarity and ease of understanding.

9. More datasets/no of stations should be provided to demonstrate the generalization ability and robustness of AI models.

10. What is new in this manuscript that was not there in the previous study? And what are the differences between this study and the old study? The limitations should be differentiated in discussion.

11. Future study must be stated.

Reviewer #3: The article titled "Forecasting Monthly Runoff in a Glacierized Catchment: A Comparison of Extreme Gradient Boosting (XGBoost) and Deep Learning Models" evaluates the effectiveness of XGBoost, LSTM, and Random Forest RF models in forecasting monthly runoff in the Lotschental catchment, Switzerland. This comparative analysis is beneficial for identifying the best model for monthly runoff forecasting under complex hydrological conditions, especially in glacierized catchments.

The article is interesting, but the following issues should be addressed.

Specific Comments and Suggestions:

1. Consider adding in Abstract a sentence highlighting the novelty of the study, such as the use of turning point analysis or the comparison of multiple machine learning models in this specific context.

2. In Introduction the literature review could be further strengthened by discussing recent advancements in machine learning and their potential applications in hydrological modeling.

The research questions could be explicitly stated at the end of the Introduction to provide a clear focus for the study.

3. Data Sources: Clearly indicate the specific sources of meteorological and hydrometric data used in the study.

4. Additional questions for consideration In the "Case Study Location" section:

- have there been any significant changes in land cover or vegetation patterns within the catchment in recent decades?

- are there any known geological faults or other factors that could influence the stability of the slopes and the risk of landslides?

- how have past flooding events affected the local communities and infrastructure?

5. Limited data temporal scope.

Although the study spans nearly two decades (2002-2021), integrating longer historical data could provide a more comprehensive understanding of long-term trends and improve the models' generalization capacity. Then the models could capture rare but critical climate events that affect runoff.

Why weren't earlier data and longer measurement series used?

6. Consider adding a more detailed explanation of the LSTM architecture

7. Discuss the process of hyperparameter tuning for each model, as this can significantly impact performance.

8. How did the choice of input lags affect the performance of the models?

9. Were any other machine learning models considered for comparison?

10. Were there any significant differences in model performance during different seasons or hydrological conditions?

11. How did the models perform in forecasting extreme events, such as floods or droughts?

12. While the study compares three individual models, it doesn’t explore hybrid modeling approaches. Combining the strengths of XGBoost and LSTM, for instance, could yield a model that balances the interpretability and robustness of XGBoost with the sequential learning advantages of LSTM.

The authors should refer to the issue of potential for hybrid model.

13. Although LSTM is a robust model for time series data, other advanced deep learning architectures, could further improve forecasting accuracy by capturing long-term dependencies more effectively. The authors should address this probleme.

14. The sensitivity of the AETP metric should be assessed to different thresholds for defining turning points. This can help to evaluate the robustness of the results.

15. Including climate projections could significantly enhance the model's utility for long-term water resource planning in glacierized regions that are highly sensitive to global warming.

16. Are there any specific regions or time periods where one model consistently outperforms the others?

17. To improve the "Discussion" section, it would be necessary to specify"

- how do the findings of this study compare to other research on runoff forecasting in glacierized catchments located in different regions?

- are there any potential biases or uncertainties associated with the use of historical data for model training and validation?

- could the XGBoost model be adapted or improved to forecast runoff at different time scales (e.g., daily, weekly)?

Incorporating longer data records, exploring hybrid models, and integrating climate change scenarios could further enhance the model's applicability in hydrological forecasting.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Forecasting Monthly Runoff in a Glacierized Catchment: A Comparison of Extreme Gradient Boosting (XGBoost) and Deep Learning Models.

PONE-D-24-40156R1

Dear Dr. Salem,

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,

Shicheng Li

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #3: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: 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 #3: (No Response)

Reviewer #4: Yes

Reviewer #5: Yes

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

Reviewer #3: (No Response)

Reviewer #4: Yes

Reviewer #5: 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 #3: (No Response)

Reviewer #4: Yes

Reviewer #5: 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 #3: (No Response)

Reviewer #4: Yes

Reviewer #5: 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 #3: (No Response)

Reviewer #4: As is been already reviewed and all the queries answer by authors successfully. Now it can be published.

Reviewer #5: The paper titled “Forecasting Monthly Runoff in a Glacierized Catchment: A Comparison of Extreme Gradient Boosting (XGBoost) and Deep Learning Models” explores monthly runoff forecasting in a glacierized catchment. The authors utilize novel forecasting models along with a robust statistical assessment method, Average Absolute Error of Turning Points (AETP). This study could be a valuable addition to the existing literature. However, there are several issues that should be addressed:

1. The authors should better emphasize the significance of monthly runoff forecasting for drought and flood monitoring in the Introduction.

2. In the Results and Discussion, the authors analyze model accuracy only at the monthly and seasonal levels. A more detailed analysis for each month is needed to highlight which months AI models forecast runoff more accurately.

3. It is advisable to apply combined accuracy metrics rather than relying solely on single statistical measures such as RMSE and MAE to assess model performance more effectively.

4. The study should include limitations and future recommendations to provide a more comprehensive discussion.

5. The article could benefit from considering additional recent relevant references in the state-of-the-art analysis on runoff forecasting, such as:

https://doi.org/10.1016/j.rineng.2024.102104

https://doi.org/10.1007/s00477-024-02760-w

https://doi.org/10.1016/j.jhydrol.2024.132175

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-->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 #3: No

Reviewer #4: Yes:  Dr Bibhuti Bhusan Sahoo

Reviewer #5: No

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