PONE-D-24-16340Building Safer and More Resilient Cities in China: A Novel Approach Using a Dynamic Nonhomogeneous Gray Model for Data-Driven Decision-MakingPLOS ONE

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Academic Editor

PLOS ONE

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Additional Editor Comments:

The manuscript has some values but should be revised based on the reviewers' comments.

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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: Partly

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

Reviewer #1: Yes

Reviewer #2: N/A

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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: Yes

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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

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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: This study provides an interesting topic with a concise overview of the research on building safety within the context of urban resilience in China. I think the main point of this research wants to convey the importance of urban resilience in the face of rapid urbanization and the necessity of developing cities that can foresee risks, reduce disaster impacts and recover swiftly from crises. The proposed dynamic nonhomogeneous gray model (DNMGM(1,1)) is clearly presented as the core methodology. Overall, the manuscript is well written, and the implementation and the experiment are an interesting contribution, but there is little original method, and research problem and significance are ambiguous. I have the following detailed concerns:

Abstract: The abstract presents a compelling case for the use of the DNMGM (1,1) model in enhancing urban resilience through better prediction and planning in building safety. While the abstract is largely comprehensive, it could briefly mention any limitations of the study or areas for future research. How the findings can be implemented by urban planners or policymakers might further enhance the practical relevance of the study?

1. Introduction:

This introduction effectively highlights the importance of the construction industry, the significant economic contribution of this sector and the pressing concerns related to construction safety. It provides a well-rounded context by discussing the economic impact, employment generation, and the societal challenges associated with construction safety incidents.

Areas for improvement: The introduction is dense with information and could benefit from clearer, more concise language in some parts. Simplifying the explanation of complex models and their limitations could enhance readability. While the introduction references several studies, integrating these references more seamlessly into the narrative could improve the flow. For instance, directly linking specific studies to the claims made about model performance can strengthen the argument. Explicitly stating the research gap (i.e., the need for a more accurate model for predicting construction fatalities) and the objective of the paper (introducing the DNMGM(1,1) model) earlier in the introduction could provide clearer direction. Briefly mentioning how the new model could be practically implemented in the construction industry and its potential real-world impact would add depth and relevance. Some transitions between paragraphs could be smoother. For example, moving from the general importance of construction safety to specific predictive models feels abrupt. A clearer structure with subheadings might help in organizing the content more logically. By enhancing clarity, conciseness, and integrating practical implications, the introduction can be further strengthened.

2. Approximate inhomogeneous dynamic GM(1,1) modeling process:

While this section is informative, some parts are dense and could be more concise. Simplifying the language and breaking down complex ideas into shorter sentences could improve readability. The transitions between discussing static models, dynamic models, and the specifics of the DNMGM(1,1) model could be smoother. Clearer subheadings might help guide the reader through the different sections. The mention of Fig. 1 is helpful, but a brief description or summary of what the figure illustrates would aid in understanding.

The theoretical justification for the DNMGM(1,1) model is strong, a brief discussion on how this model could be practically implemented in the construction industry would add value. For example, mentioning specific steps or tools for integrating this model into existing safety protocols could enhance its practical relevance. Providing hypothetical or actual examples of how the DNMGM(1,1) model has improved prediction accuracy in comparison to other models would make the argument more compelling.

3. Forecasting of the death risk in China:

This section provides a good overview of the historical trends in construction accident fatalities in China and the rationale for using the gray prediction model. Redundant phrases should be eliminated. Here, authors briefly mentioned various indicators but does not explain why fatalities are the primary focus. A brief justification would be helpful. More detailed description of what Fig. 2 shows would help readers who cannot see the figure immediately. Moreover, providing more context about the data source and type would strengthen the credibility of the data.

4. Static prediction results and analysis:

Some sentences are lengthy and complex, which can affect readability. The explanation of what Fig. 3 and Fig. 4 depict could be more detailed. This would help readers understand the visual data better. Providing a bit more context about why certain models performed better or worse could enhance the reader's understanding of the underlying reasons for these differences. The transition between discussing the performance of the models and the final conclusion could be smoother.

5. Dynamic prediction results and analysis:

Ensure that all technical terms and abbreviations, such as MAPE and DNGM(1,1), are clearly defined when first introduced. Include direct references to the figures in the text to guide the reader (e.g., "As shown in Fig. 5"). Provide a brief explanation of why MAPE is used as the evaluation metric and how it is calculated. Expand the description of each model (GM, DNGM, MGM, DNMGM) to provide more context on their differences and similarities.

By incorporating these revisions, the section will be more informative and easier for readers to understand the significance and implications of the dynamic adaptive capability and generalizability of the DNMGM(1,1) model.

Ensure consistent use of model names (for e.g. Gray/ Grey) and terms throughout the text. Include direct references to figures and tables within the text to guide the reader (e.g., "As shown in Table 1"). Overall, improve the structure and flow of the sections to enhance readability and logical coherence, ensuring smooth transitions between paragraphs and sections. Correct minor typographical errors (e.g., "is or observed data" should be "or observed data is" and Instead of he "The").

Reviewer #2: 1.The use of the dynamic nonhomogeneous gray model (DNMGM(1,1)) represents a significant advancement in predictive modeling for urban resilience. The ability of this model to integrate new data in real-time enhances its applicability in rapidly changing urban environments.

2.The study effectively validates the DNMGM(1,1) model using real-world data on construction and traffic accident fatalities. The reported average relative errors demonstrate the model's superior accuracy compared to traditional gray models, reinforcing its potential for practical implementation.

3.What specific parameters were used in the DNMGM(1,1) model, and how were they determined? Could the choice of these parameters affect the model’s predictive accuracy?

4.What were the primary sources of the data used for model validation? How was data quality ensured, and were there any significant challenges in obtaining reliable data for this study?

5.The research underscores the versatility of the DNMGM(1,1) model by demonstrating its efficacy in different domains, such as construction and traffic safety. This highlights the model's robustness and potential for widespread use in various aspects of urban planning and risk management.

6.How does the DNMGM(1,1) model compare with other advanced predictive models, such as machine learning algorithms, in terms of accuracy and computational efficiency? Are there scenarios where other models might be more suitable?

7.Given the model’s sensitivity to abnormal data fluctuations, what specific strategies or combination methods are being considered to enhance its robustness? How might these strategies be implemented in future research?

8. It is suggested to add articles entitled “Baxhuku et al. New Law Enforcement Impact on the Prevention of Road Accidents in Kosovo”, “Balal et al. Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock” and “Al-Abayechi & Al-Khafaji. Forecasting the Impact of the Environmental and Energy Factor to Improve Urban Sustainability by Using (SEM)” to the literature review.

9.The conclusion suggests future research should focus on combining the DNMGM(1,1) model with other predictive methods to address its limitations in handling abnormal data fluctuations. This proactive approach is commendable and necessary for enhancing the model's reliability and applicability.

10.The study presents its findings in a clear and structured manner, making it easy to follow the progression from model development to validation and application. This clarity is essential for understanding the model's capabilities and limitations.

11.What are the practical implications of implementing the DNMGM(1,1) model in urban planning and disaster management? Are there any case studies or pilot projects that demonstrate its real-world applicability and benefits?

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Reviewer #1: Yes: Rahisha Thottolil

Reviewer #2: No

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Submitted filename: PONE-D-24-16340_Review.pdf

Building Safer and More Resilient Cities in China: A Novel Approach Using a Dynamic Nonhomogeneous Gray Model for Data-Driven Decision-Making

PONE-D-24-16340R1

Dear Dr. FENG,

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,

Qing-Chang Lu

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 #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 #2: Yes

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

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 #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 #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 #2: Review Comments to the Author: Acceted for publication this version. Review Comments to the Author: Acceted for publication this version.

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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 #2: No

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