We would like to express our sincere gratitude for your professional advice. These comments help to improve rigor of our study. We have carefully considered your comments and have thoroughly revised the manuscript according to your suggestions. This study is a deep dive into the realm of interdisciplinary research, incorporating theories and methods from various disciplines. We acknowledge that while such interdisciplinary integration offers a broad perspective and enriches insights, facing challenges in articulating it systematically is inevitable. We regret any parts where we have not clearly explained. We are committed to further refine our article to ensure its presentation is cogent. We would like to show the details as follows:

1. Traditional evacuation path planning methods are generally based on static scenarios and rely on manually drawn lines from a macro perspective, which makes it difficult to achieve road-level precision, lacking timeliness and availability. The benefits of using deep reinforcement learning to address path planning issues lies in its ability to automatically select safe roads and plan appropriate evacuation routes for affected individuals based on their limited surrounding environment. This approach can adapt to dynamically changing disaster environments, providing guidance for individuals during storm surge disasters. We will enhance the introduction of the benefits of using DRL, and highlight the innovation of this work.
2. Considering your feedback on the excessive number of figures and the need for more detailed information, we decided to merge Figures 1 and 4 and added more descriptive captions to all figures. We believe these improvements will more effectively convey the information and enhance the overall quality of the article.
3. We had actually used tidal observation data from hydrological stations to validate the ADCIRC+SWAN model. The reason why this part of the verification was not included in the main text is primarily because the focus of this study leans more towards evacuation path planning rather than storm surge simulation. The verification of tidal levels is not as crucial. We will include the validation results for the ADCIRC+SWAN model in the appendix.
4. We will move the content you identified as belonging to the methodology section from the results section to ensure the logical flow and coherence of the sections.
5. The Markov Decision Process is the mathematical foundation of the DQN algorithm. To leverage the advantages of deep reinforcement learning, we transform the path planning problem into a Markov Decision Process problem, and then employed the DQN model to address it. We apologize for not making this part clear in the article and we will enhance the explanation of the relationship between the Markov Decision Process and the DQN algorithm in the text.
6. The optimal path is obtained through the method of exhaustive search. We define the optimal path as the route where the agent, starting from the initial cell, achieves the maximum cumulative reward, hence the exhaustive search method can be used to find the optimal path for a given start. This aspect was not detailed in the main text, making our work appear perplexing and sloppy. We will revise this section to include the relevant information.
7. We will integrate the full name "Deep Reinforcement Learning (DRL)" into the manuscript's title to more accurately reflect the content of the study.

Thank you again for your suggestions, these will help in improving the manuscript, and we will ensure there are no grammatical errors in the revised manuscript.

We deeply appreciate your pointing out the areas where our manuscript requires further substantiation, and your constructive suggestions for improvements. Your comments are invaluable in improving our manuscript and advancing research in the field. We are committed to addressing each of the concerns raised and have worked diligently to revise our manuscript accordingly. Taking the Daya Bay Petrochemical Industrial Zone as an example, this study integrates risk assessment with the road network and employs deep reinforcement learning algorithm for evacuation path planning. It can provide effective guidance for affected individuals based on the limited surrounding environment. This study focuses on the evacuation path planning for storm surges, and thus the description of the storm surge simulation part is rather brief, rendering the article somewhat sketchy and unclear. We will make revisions according to the suggestions provided, as follows:

1. As suggested, we will revise the abstract to include the study's aim after the research gap. This addition clarifies our research objectives and enhances the abstract's coherence.
2. In the introduction, we will provide a detailed rationale for employing the ADCIRC+SWAN model, highlighting its advantages over other models. The ADCIRC+SWAN model is currently a more mature model used for storm surge simulations, incorporating both wave and current factors, with more accurate simulations of storm surges. Additionally, the two models share a same grid for synchronous coupling, simplifying usage.
3. There are already some studies that employ DRL methods for evacuation path planning. However, this study represents the first application of DRL to incorporate storm surge risk assessment within large-scale raster environments for this purpose. We will add details of other related works in the introduction, summarize the differences between our research and others, and clarify the innovations of our study.
4. In the introduction, we will delineate the specific objectives and highlight the significance of our study in addressing research gaps in storm surge evacuation. By summarizing our works, we aim to offer readers a concise overview of the unique contributions of our research.
5. We will update the Introduction section to explicitly state the original contributions and limitations of our current research, ensuring transparency and setting the stage for future work in this area.
6. The initial draft of this article was actually completed in 2022, and therefore does not include related research in 2023 and 2024. We will update the literature review to include the latest advancements and discussions in the field.
7. In the South China Sea, the predominant tidal components are four diurnal and four semidiurnal components. We have also included three additional components, which actually have a minor impact on the simulation results. Given that our research concentrates on storm surge risk assessment and evacuation path planning, the precision requirements for storm surge simulation are less stringent. Consequently, we employed these 11 tidal components.
8. We had calibrated and validated the ADCIRC+SWAN model using tidal observation data from hydrological stations. The exclusion of this calibration part from the main text is primarily because our study is focused more on evacuation path planning than on storm surge simulation. Therefore, the verification of tidal levels was considered less critical. We will include the calibration results for the ADCIRC+SWAN model in the appendix.
9. This study focuses on the risk assessment and the evacuation path planning. In setting up of the coupled model, we adopted several existing parameterization schemes and obtained marginally satisfactory simulation results. Given that our focus is not on the numerical simulation of storm surges, we did not perform a parameter sensitivity analysis of the numerical model.
10. We will enhance the conclusion section by providing a concise summary of the key findings and discussing the applicability of this work in practical decision-making processes.

    We hope these responses address the concerns raised. We are grateful for the opportunity to enhance our work based on the valuable suggestions received and look forward to the potential contribution of our study to the literature in this area of research.