Abstract—Groundwater Level (GWL) control is crucial for
sustainable water resource management, agricultural planning,
and reducing environmental impacts. GWL forecasting is
crucial for optimizing water usage, preventing over-extraction,
addressing land subsidence, supporting ecological balance, and
making informed decisions for economic activities. However,
GWL variability is driven by multiple factors, including
precipitation, soil properties, seasonal fluctuations, and human
activities, which complicate prediction efforts. To address these
challenges, advanced machine learning models, such as Long
Short-Term Memory (LSTM) networks and Gated Recurrent
Units (GRU), have proven effective in capturing temporal
dependencies and handling non-linear relationships in time
series data. These models are ideal for identifying patterns over
extended periods and managing complex interactions
influencing GWL. In this study, we utilize LSTM and GRU
models to predict GWL, developing four sub-models for each:
an hourly, daily, 10-day, and monthly model, all aimed at
forecasting GWL one month in advance. The models’
performance is evaluated using statistical metrics such as Mean
Square Error (MSE), Mean Absolute Error (MAE), and Rsquared.
The daily LSTM and GRU models achieved R-squared
values of 69.87% and 71.34%, respectively. Their exceptional
predictive capabilities in groundwater forecasting provide
valuable insights and contribute significantly to the field of
water resource management.
Keywords—groundwater level, LSTM, GRU, machine
learning, deep learning
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.
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).