Using Artificial Intelligence Techniques for Reconstructing GRACE Data Over Nile River

Basma  Fawzi; Mahmoud  Salah; Mahmoud  El-Mewafi

doi:10.6180/jase.202508_28(8).0001

Using Artificial Intelligence Techniques for Reconstructing GRACE Data Over Nile River

Basma Fawzi¹This email address is being protected from spambots. You need JavaScript enabled to view it., Mahmoud Salah², and Mahmoud El-Mewafi³

¹Department of Civil Engineering, Delta Higher Institute for Engineering and Technology, Mansoura 35111, Egypt

²Department of Geomatics, Faculty of Engineering Shoubra, Benha University 13518, Egypt

³Public Works Department, Faculty of Engineering, Mansoura University 35111, Egypt

Received: July 3, 2024
Accepted: September 19, 2024
Publication Date: October 26, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202508_28(8).0001

Terrestrial water storage (TWS) is crucial for the worldwide hydrologic water cycle and sustainability of water. Gravimetric missions such as the Gravity Recovery and Climate Experiments (GRACE) & GRACE-Follow on (GRACE-FO) are essential for evaluating changes in TWS.This study introduces a new approach by combining remote sensing data in the form of mascon data with deep learning models (DL) such as the Long Short-Term Memory (LSTM) Model to reconstruct GRACE data in the Nile River basin (NRB) from 2002 to 2022 to study the changes in water storage with high accuracy. This research strategy depends on applying several convolutional neural network (CNN) models, including AlexNet, VGG Net, and GoogleNet, for extracting features from time series GRACE data. After that, use the optimization algorithm (DTOFGW) to get the best hyperparameters for LSTM. Finally, comparing many different optimization algorithms showed the proposed model’s superiority. Applying statistical analysis tests illustrated the significance of our proposed model, such as ANOVA and T-test. The results of the trial showed that the proposed model (DTOFGW-LSTM) outperformed the other models by an accuracy of 96.8%, sensitivity of 0.5, specificity of 99.5%, P value of 0.85, N value of 0.97, F-score of 0.63, and confidence value of 97%.

Keywords: GRACE; GRACE-FO; Artificial Intelligence; LSTM-DTOFGW; ANOVA; T-test

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