Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE/GRACE-FO Gravimetric Data and Machine Learning
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 variation...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/1/50 |
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| Summary: | 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. |
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| ISSN: | 2073-4433 |