Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Need to be minor updation. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The paper must be accepted for publication but only after minor revisions. 

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the comments concerning our manuscript  (ID: 3028503). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made corrections which we hope meet with approval. Please refer to the Word and PDF versions of the newly submitted manuscript for details.

Thanks again!

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The proposal is interesting, however there are several aspects that must be improved.

1. Implementation of deep learning models require data subsets such as training, validation and testing, this information was not provided.

2. Machine learning and deep learning models predict based on prediction steps. This information is missing  in the manuscript. The authors should experiment with 1, 2, 3 or more steps.

3. The characteristics of the data should be shown, mean, std, max, min,... as well as a graphical view.

4. Missing values were estimated by linear interpolation. It must be justified, considering that there are other techniques such as spline, IDW, moving averages, among others.

5. The proposal results should be compared with benchmark models such as LSTM, GRU, BiLSTM, BiGRU, RNNs with Attention layers, ...

6. To evaluate the predictions, metrics such as RMSE, MAPE, R2,... must be included.

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7. "Study on Improvement of the Low Visibility Forecast over Guizhou，China based on Muti-varibale Deep Learning Model"

It says "Multi-varibale" must say "Multi-variable"

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the comments concerning our manuscript  (ID: 3028503). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made corrections which we hope meet with approval. Please refer to the Word and PDF versions of the newly submitted manuscript for details.

Thanks again!

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

I thoroughly reviewed your manuscript, The topic of the manuscript is excellent, however, I suggested some comments for you. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Dear Authors,

The qualities of English need improvement. 

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the comments concerning our manuscript  (ID: 3028503). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made corrections which we hope meet with approval. Please refer to the Word and PDF versions of the newly submitted manuscript for details.

Thanks again!

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Please find the attached file.

Comments for author File: Comments.pdf

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the comments concerning our manuscript  (ID: 3028503). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made corrections which we hope meet with approval. Please refer to the Word and PDF versions of the newly submitted manuscript for details.

Thanks again!

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Most of the recommendations have been implemented, however, the one corresponding to the design of the experiments still has deficiencies. Only two partitions were considered for the experiments (training and validation) when for this type of models three partitions must be considered (training, validation, and testing)

Author Response

Point 1: Most of the recommendations have been implemented, however, the one corresponding to the design of the experiments still has deficiencies. Only two partitions were considered for the experiments (training and validation) when for this type of models three partitions must be considered (training, validation, and testing)

Response 1: Sorry for the inappropriate expression and misleading. In fact, for quality model training and better forecasting skills in limited samples, the dataset was divided into training, validation, and testing with a 16:1:9 ratio. We have revised lines 215-218 of the manuscript. Furthermore, we will consider this valuable feedback in our future work.