Thu Aug 14, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Hamdi, Mohamed, El Alem, Anas, and Goita, Kalifa, 2025. Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE/GRACE-FO Gravimetric Data and Machine Learning. Atmosphere, 16(1):50, doi:10.3390/atmos16010050.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025Atmos..16...50H,
author = {{Hamdi}, Mohamed and {El Alem}, Anas and {Goita}, Kalifa},
title = "{Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE/GRACE-FO Gravimetric Data and Machine Learning}",
journal = {Atmosphere},
keywords = {climate change, groundwater storage, remote sensing, machine learning, groundwater potential map, Saskatchewan River Basin},
year = 2025,
month = jan,
volume = {16},
number = {1},
eid = {50},
pages = {50},
abstract = "{Climate change is having a significant impact on groundwater storage,
affecting water resources in many parts of the world. To
characterize this impact, remote sensing and machine learning
are essential tools to analyze the data accurately and
efficiently. This study aims to predicting the variations of
groundwater storage (GWS) using GRACE/GRACE-FO and multi-source
remote sensing data, combined with machine learning techniques.
The approach was applied over the Canadian Prairies region. The
study area was classified into three zones of different aquifer
potentials (low, medium, and high) using a combination of remote
sensing data and the Classification and Regression Trees (CART)
approach. The prediction model was developed using a machine-
learning approach based on multiple linear regression to
estimate GWS variations as a function of various environmental
parameters. The results showed that the developed model was able
to predict GWS variations with satisfactory accuracy (up to 95\%
of the explained variance) and good robustness (96\% success
rate). They also provided a better understanding of the
variations in groundwater storage in the Canadian Prairies.
Therefore, this work provides a promising method for predicting
GWS, which could eventually be applied to other similar
environmental conditions.}",
doi = {10.3390/atmos16010050},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025Atmos..16...50H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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