Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article is titled "Development and application of an optimization-based method for groundwater vulnerability assessment." In this study, the authors developed an optimization-based approach, PSO-BP-DRASTIC, and applied it in a coastal city in China. The authors systematically compared and evaluated three different models using ten-fold cross-validation and nitrate concentration data. The article is interesting, but in my opinion, it requires improvement.

My comments are as follows:

- The introduction is quite well-written, but an explanation of why the authors used nitrate concentrations is missing.

- Subsection 2.2 requires further clarification. The authors should explain the preprocessing and quality control involved. Furthermore, the model should be described in detail so that it can be applied to other studies.

- Section 2.3 requires further clarification. The technical framework does not reflect the tasks performed by the authors. The descriptions of the individual models do not refer to the literature. The authors classified the vulnerability index into five categories – low, relatively low, moderate, relatively high, and high, but they did not specify the criteria used to determine these categories.

- Section 4 is a discussion, but the authors do not present their research results in relation to those of other authors. This section should clearly indicate the advantages and disadvantages of the proposed solution. Compare it with research by other authors and, above all, cite the literature.

- the references list is sparse and requires supplementation with new sources.

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The study aims to develop an optimization-based approach that enables modification of the weighting and ratings in the traditional DRASTIC model. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the Particle Swarm Optimization (PSO) algorithm to refine the BP-NN's initial weights and thresholds. The developed model was implemented in a coastal city in Chia. Results demonstrate that the proposed model achieves high classification accuracy on the test set and shows a strong correlation with site-specific observations of Nitrate concentration. I have followed the study's concept and the logic of the presentation presented in the manuscript. In general, the manuscript is well organized and presents its logic clearly. I would suggest a moderate modification applied to the manuscript. The following is a list of my comments for the manuscript:

1. The study aims to develop a neural network algorithm to modify the weightings for the traditional DRASTIC model. Many other DRASTIC approaches seek to incorporate additional factors, such as land use or land cover, to alter the conventional DRASTIC approaches. These reviews might be essential to add to the introduction.
2. The number of data points would be critical for training and testing in the neural network approaches. It is not clear from the manuscript how to determine the appropriate number of data points for the application to a new site. General guidance and suggestions will be helpful for the applications.
3. Table 2 lists important weightings from different models. The A and I factors in neural network-based approaches and the traditional DRASTIC model differ in their weightings. Specifically, the neural network-based models place greater weight on A and relatively low weight on I; however, traditional DRASTIC assigns higher weight to I than to A. Why? To me, actors A, I, and C could be relevant physical factors for calculating the vulnerability. Would neural network-based models provide a better explanation of the site-specific conditions?
4. There must be some behavior that the vulnerability is highly correlated with the land use or land cover. Please add the discussion for the results.

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. The authors should revise the research topic to ensure it is more tightly linked to coastal settings.
2. Line 101: It would be more effective to use Yantai as a specific example to illustrate a particular setting, such as a coastal environment.
3. The geological and hydrogeological setting is inadequately described and requires substantial expansion.
4. The novelty of this study should be articulated more clearly in both the abstract and introduction sections.
5. The study area encompasses diverse terrain types and stratigraphic lithologies. How did the authors account for these variations in their assessment?
6. The discussion section lacks depth and should be substantially enhanced. A more thorough analysis is required to adequately address the study's novel contributions, as is expected in academic publications.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

the authors corrected the article

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript has been greatly improved. But the following issues should be addressed before it can be considered for accepting.

1. Avoid to use “we” in the academic papers.
2. For conclusions section: tell the results, findings and conclusions directly without subtitles. The organization of the conclusions section should be reconsidered.
3. Emphasize the scientific significance and novelty in the abstract and introduction sections.

Author Response

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Author Response File: Author Response.pdf