Research on Evaluation of University Emergency Management Ability Based on BP Neural Network
Round 1
Reviewer 1 Report
This is a carefully done study and the findings are of considerable interest. This manuscript is recommended for publication by addressing the following minor revisions.
1. In Introduction Section, please describe in detail the motivation behind this work.
2. The current manuscript needs to be polished by a native English speaker or a professional language editing service.
3. Please further improve the theoretical basis of evaluation index system construction?
4. There is a problem with the format of Table 1, please modify it.
5. For the results presented in the Figures in the simulation, more explanations on them seem necessary and helpful to readers.
6. The conclusion should be more concise.
Author Response
Dear Editors and Reviewers:
Thank you for your letter and the reviewers' comments concerning our manuscript entitled " Research on Evaluation of University Emergency Management Ability Based on BP Neural Network" (ID: ijerph- 2181201). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction using the revision model. In the meantime, we consulted a professional editing service and asked several colleagues who are native English editors to help polish our article. In addition, we confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to International Journal of Environmental Research and Public Health. Furthermore, the point-to-point responses have also been presented in this letter.
Response to reviewer 1:
Comment 1: In Introduction Section, please describe in detail the motivation behind this work.
Response 1: I have refined the motivation behind this study in the introduction section.
Comment 2: The current manuscript needs to be polished by a native English speaker or a professional language editing service.
Response 2: We have polished the language by the editing service to improve readability of the manuscript.
Comment 3: Please further improve the theoretical basis of evaluation index system construction?
Response 3: I have perfected the theoretical basis of evaluation index system construction in the first part.
Comment 4: There is a problem with the format of Table 1, please modify it.
Response 4: I checked Table 1 carefully and modified the format.
Comment 5:For the results presented in the Figures in the simulation, more explanations on them seem necessary and helpful to readers.
Response 5: For the simulation diagram in the paper, we have improved more explanations for readers to understand.
Comment 6: The conclusion should be more concise.
Response 6: The conclusion has been simplified.
Reviewer 2 Report
Comments for author File: 
Comments.pdf
Author Response
Dear Editors and Reviewers:
Thank you for your letter and the reviewers' comments concerning our manuscript entitled " Research on Evaluation of University Emergency Management Ability Based on BP Neural Network" (ID: ijerph- 2181201). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction using the revision model. In the meantime, we consulted a professional editing service and asked several colleagues who are native English editors to help polish our article. In addition, we confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to International Journal of Environmental Research and Public Health. Furthermore, the point-to-point responses have also been presented in this letter.
Response to reviewer 2:
Comment 1: The summary should be more concise.
Response 1: The summary has been simplified.
Comment 2: The meaning of line 110 is ambiguous, please confirm.
Response 2: The statement of " Through combing a large amount of domestic and foreign related literature "has been changed to " By combing and analyzing a large amount of domestic and foreign related literature " and this sentence is currently on line 125 in the revised version.
Comment 3:Why should psychological crisis prevention and counseling ability be mentioned in the post recovery ability in the evaluation index system of emergency management ability in colleges and universities?
Response 3: After the occurrence of an emergency, people's psychology will undergo significant changes. Psychological crisis prevention and counseling can effectively help them resolve the impact and damage brought by the crisis events, and avoid and resolve extreme events, which has important practical significance. Therefore, the psychological crisis prevention and counseling capability should be mentioned in the post recovery ability.
Comment 4: Fig 2 is not clear, a better one should be provided.
Response 4: We have edited the Fig 2 significantly to clarify a few details that were not sufficiently clear in the original manuscript.
Comment 5: Why is it necessary to de-normalize the results of the predicted output?
Response 5: When the BP neural network model is established and trained, the input data are normalized. In order to better compare the predicted output with the real value, the predicted output results need to be de-normalized.
Comment 6: How is the comparison between the predicted output and the expected value plotted?
Response 6: Based on MATLAB platform, the figure function is used to draw the visual prediction results.
Comment 7: What are the advantages of this research?
Response 7: Based on BP neural network method and MATLAB platform, an evaluation model of university emergency management ability was constructed. The results show that it is feasible to apply the evaluation model based on BP neural network to the emergency management ability of colleges and universities, which provides a new method for the evaluation of emergency management ability of colleges and universities.
Reviewer 3 Report
Please check the attachment
Comments for author File: Comments.pdf
Author Response
Dear Editors and Reviewers:
Thank you for your letter and the reviewers' comments concerning our manuscript entitled " Research on Evaluation of University Emergency Management Ability Based on BP Neural Network" (ID: ijerph- 2181201). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction using the revision model. In the meantime, we consulted a professional editing service and asked several colleagues who are native English editors to help polish our article. In addition, we confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to International Journal of Environmental Research and Public Health. Furthermore, the point-to-point responses have also been presented in this letter.
Response to reviewer 3:
Comment 1: Can authors explain why BP neural network is chosen to study the evaluation of university emergency management ability?
Response 1: Compared with analytic hierarchy process and fuzzy comprehensive evaluation method, BP neural network can overcome the subjectivity existing in the process of assigning weight to evaluation indicators, and at present, few people use BP neural network to study the evaluation of university emergency management ability. This is why we choose BP neural network to study the evaluation of university emergency management ability.
Comment 2: Why choose MATLAB platform for the establishment of the BP model and training?
Response 2: In the establishment and training of BP model, the main reasons for selecting MATLAB platform are as follows: MATLAB toolbox can provide a wealth of built-in functions, users can call the corresponding program function to set the network structure and parameters according to their actual needs, which greatly reduces the trouble of compiling huge program code.
Comment 3: Why do you use mapminmax function to normalize the data when you build and train model based on MATLAB platform?
Response 3: The reasons for using mapminmax function to normalize data are as follows: it can prevent input data from falling into the saturated region, while maintaining the characteristic values of original data, and uniformly process all kinds of data in different dimensions and convert them into values between [0,1].
Comment 4: What is the meaning of the mean absolute percentage error?
Response 4: The mean absolute percentage error is a relative error measure that can use absolute values to avoid positive and negative errors cancelling each other out and thus predicting the accuracy of the model. The closer the mean absolute percentage error is to zero, the more accurate the model is.
Comment 5: Please explain the advantages of the BP neural network in this study.
Response 5: The advantages of BP neural network are as follows: On the one hand, the creation, training and use of neural networks can be completed through MATLAB to realize the evaluation and analysis of data. On the other hand, it has strong logical processing ability and sufficient fault tolerance ability.
Comment 6: Future research direction will be shown in Conclusions.
Response 6: I have perfected the future research direction in the conclusion part.
Comment 7: In the Introduction, some important references about BP neural network and its application are missing, and the authors should supplement them.
Response 7: We have checked the references carefully and supplemented some important references about BP neural network and its application in the introduction.
We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. We appreciate for Editor and Reviewers ' warm work earnestly, and hope that the correction will meet with approval.
Once again, thank you very much for your comments and suggestions.
Best wishes.
Kind regards,
Ms. Rui Hu
