the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements
Fawzi Zarzoura
Mosbeh Kaloop
Pijush Samui
Jong Wan Hu
Md Shayan Sabri
Tamer ElGharbawi
Abstract. This study investigates the use of new machine learning techniques in mapping variation in ground levels based on ordinary spirit levelling (SL) measurements. Convolution Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and bi-directional LSTM (BI-LSTM) were developed and compared in the current study to estimate the leveling through SL measurements. SL measurements of the Manzalla region, Egypt, were used in the current study. 3253 datasets of SL observation points, including 229 benchmarks of precise levelling (PL), were used to design and verify the proposed model’s results. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The root mean square error and correlation determination of the LSTM model are 7.4 cm and 0.99, respectively, in the testing stage. The accuracy of mapping ground levelling through the developed LSTM model is close to 99 % in terms of model error.
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Fawzi Zarzoura et al.
Status: open (until 10 Jul 2023)
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CEC1: 'Comment on gmd-2023-62', Juan Antonio Añel, 06 May 2023
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it severely infringes the "Code and Data Policy" of our journal.
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have not published the code that you use in your manuscript and which is necessary to replicate your work. What is more, your manuscript does not even includes the mandatory "Code Availability" section. In this way, we will have to reject your manuscript for publication unless you reply to this comment with the information about the repository (DOI and link) for the code (one acceptable according to our policy - please, check it carefully).
I should note that, actually, your manuscript should not have been considered in our journal, given this lack of compliance with our policy. Therefore, the current situation with your manuscript is irregular.
Please, address and solve this issue in a prompt manner, replying with the necessary information.
Also, in case you reply to this comment with the necessary information, and we consider this problem solved, remember that you must include in any potentially reviewed version of your manuscript the modified 'Code and Data Availability' section, with the DOI of the code.
Moreover, I should note that the data added as supplementary material is in .xlsx format, which is a format that depends on proprietary software to access the data. It would be better if you save the data in an open ISO standard format, such as .ods, or even .dat or .csv. Also, the supplementary material only contains the data, without any explication about its structure, what is each column or row, etc. Please, you should add some metadata, maybe in an additional Readme file.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2023-62-CEC1 -
AC1: 'Reply on CEC1', Jong Hu, 14 May 2023
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Dear Editor
Thank you for your comment. Please check the attachment. We have submitted the codes and datasets.
Regards
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CEC2: 'Reply on AC1', Juan Antonio Añel, 16 May 2023
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Dear authors,
Many thanks for sharing the code. Doing it as supplementary material is enough. However, I would recommend you change the name "file" in the Python code and use the actual name of the file.
Please, if your manuscript is accepted for publication or goes through additional review stages, do not forget to update the supplementary material with the files attached to this comment and add the Code Availability section to the manuscript.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2023-62-CEC2 -
AC2: 'Reply on CEC2', Jong Hu, 16 May 2023
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Dear Editor
Thank you for your comment; we will add the "code availability" section in the paper with the revised version form.
My best regards
Citation: https://doi.org/10.5194/gmd-2023-62-AC2
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AC2: 'Reply on CEC2', Jong Hu, 16 May 2023
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CEC2: 'Reply on AC1', Juan Antonio Añel, 16 May 2023
reply
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AC1: 'Reply on CEC1', Jong Hu, 14 May 2023
reply
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RC1: 'Comment on gmd-2023-62', Junye Wang, 04 Jun 2023
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The authors nvestigated mapping variation in ground levels using machine learning techniques. They compared the ordinary spirit levelling (SL) measurements using four MLs, including Convolution Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and bi-directional LSTM (BI-LSTM), in the Manzalla region, Egypt. Their results showed the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. While the subject is relevant and interesting, some details have been missing. Furthermore, it is unclear what are novelties of this paper. The authors should highlight the novelties of this paper. The reviewer lists some comments as the below:
- This paper presents a comparison of estimating SL using several MLs. However, there are many ML algorithms. It is unclear why the authors selected these methods. What advantages of every method did you select them?
- L77-82, these should be merged with Method section since they are description of methods.
- L86-124, the following paragraphs are a brief description of several methods rather than a theory summary. I would suggest you add more details of every algorithm used in this study.
- It is unclear how you implement the simulations, such as training, testing, input and output variables. You should present what software of MLs have been used for this study if your codes were not in-house. The authors should add a section to present your implementation or describe them after each algorithm.
- L229-232, what means E, N and Z in Table 1? Why did you not use input and output variables that have physical meaning?
- In Table 2, how did you calculate “Total Score”?
- I would suggest you add a figure to show results of CNN and RNN.
Citation: https://doi.org/10.5194/gmd-2023-62-RC1
Fawzi Zarzoura et al.
Fawzi Zarzoura et al.
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