Preprints
https://doi.org/10.5194/gmd-2023-62
https://doi.org/10.5194/gmd-2023-62
Submitted as: development and technical paper
 | 
12 Apr 2023
Submitted as: development and technical paper |  | 12 Apr 2023
Status: this preprint has been withdrawn by the authors.

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, and 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.

This preprint has been withdrawn.

Fawzi Zarzoura et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2023-62', Juan Antonio Añel, 06 May 2023
    • AC1: 'Reply on CEC1', Jong Hu, 14 May 2023
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 16 May 2023
        • AC2: 'Reply on CEC2', Jong Hu, 16 May 2023
  • RC1: 'Comment on gmd-2023-62', Junye Wang, 04 Jun 2023
    • AC3: 'Reply on RC1', Jong Hu, 24 Jun 2023
  • RC2: 'Comment on gmd-2023-62', Deepak Subramani, 05 Sep 2023
    • AC4: 'Reply on RC2', Jong Hu, 06 Sep 2023
  • EC1: 'Comment on gmd-2023-62', Deepak Subramani, 07 Sep 2023
    • AC5: 'Reply on EC1', Jong Hu, 07 Sep 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2023-62', Juan Antonio Añel, 06 May 2023
    • AC1: 'Reply on CEC1', Jong Hu, 14 May 2023
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 16 May 2023
        • AC2: 'Reply on CEC2', Jong Hu, 16 May 2023
  • RC1: 'Comment on gmd-2023-62', Junye Wang, 04 Jun 2023
    • AC3: 'Reply on RC1', Jong Hu, 24 Jun 2023
  • RC2: 'Comment on gmd-2023-62', Deepak Subramani, 05 Sep 2023
    • AC4: 'Reply on RC2', Jong Hu, 06 Sep 2023
  • EC1: 'Comment on gmd-2023-62', Deepak Subramani, 07 Sep 2023
    • AC5: 'Reply on EC1', Jong Hu, 07 Sep 2023

Fawzi Zarzoura et al.

Fawzi Zarzoura et al.

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This preprint has been withdrawn.

Short summary
The study aims to map variation in ground levels based on ordinary spirit levelling (SL) measurements. New machine learning techniques were developed and compared in the current study to estimate the leveling through SL measurements. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The accuracy of mapping ground levelling through the developed LSTM model is close to 99 % in terms of model error.