doi: 10.18178/ijesd.2025.16.3.1527
Application of Machine Learning Models to Predict Groundwater Level Change, a Case Study of Vung Tau City, Ba Ria-Vung Tau Province
Trinh Tuyet Vu1
,
Au Hai Nguyen1,2,* ,
Nhi Tuyet Thi Pham1 ,
Vy Minh Hong Tat1 ,
Linh Khanh Luu1
- 1The Institute for Environment and Resources, 142 To Hien Thanh Street, District 10, Ho Chi Minh City, Vietnam
- 2Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
- Manuscript receivedNovember 11, 2024
- revisedFebruary 8, 2025
- acceptedMarch 24, 2025
- publishedJune 6, 2025
Abstract
Groundwater Level (GWL) control is crucial forsustainable water resource management, agricultural planning,and reducing environmental impacts. GWL forecasting iscrucial for optimizing water usage, preventing over-extraction,addressing land subsidence, supporting ecological balance, andmaking informed decisions for economic activities. However,GWL variability is driven by multiple factors, includingprecipitation, soil properties, seasonal fluctuations, and humanactivities, which complicate prediction efforts. To address thesechallenges, advanced machine learning models, such as LongShort-Term Memory (LSTM) networks and Gated RecurrentUnits (GRU), have proven effective in capturing temporaldependencies and handling non-linear relationships in timeseries data. These models are ideal for identifying patterns overextended periods and managing complex interactionsinfluencing GWL. In this study, we utilize LSTM and GRUmodels to predict GWL, developing four sub-models for each:an hourly, daily, 10-day, and monthly model, all aimed atforecasting GWL one month in advance. The models’performance is evaluated using statistical metrics such as MeanSquare Error (MSE), Mean Absolute Error (MAE), and Rsquared.The daily LSTM and GRU models achieved R-squaredvalues of 69.87% and 71.34%, respectively. Their exceptionalpredictive capabilities in groundwater forecasting providevaluable insights and contribute significantly to the field ofwater resource management.
Keywords
- groundwater level
- LSTM
- GRU
- machine learning
- deep learning
How to Cite
Trinh Tuyet Vu, Au Hai Nguyen, Nhi Tuyet Thi Pham, Vy Minh Hong Tat, and Linh Khanh Luu, "Application of Machine Learning Models to Predict Groundwater Level Change, a Case Study of Vung Tau City, Ba Ria-Vung Tau Province," International Journal of Environmental Science and Development, vol. 16, no. 3, pp. 202-213, 2025. https://doi.org/10.18178/ijesd.2025.16.3.1527
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).