Water and Irrigation Management

Water and Irrigation Management

Groundwater Level Forecasting of Data-Scare areas using a Deep Learning Long-Short Term Memory (LSTM) Model (Case Study: Saadatabad study area, Fars Province)

Document Type : Research Paper

Authors
Mohammad Javad Aghaei
Hamed Ketabchi
Department of Water Engineering and Management, Tarbiat Modares University, Tehran, Iran.
10.22059/jwim.2025.389999.1208
Abstract
Groundwater level forecasting is a crucial approach in water resource management and planning. This forecasting can assist in improving water resource management, especially in regions facing water crises. In recent years, the use of artificial intelligence for forecasting groundwater levels has gained significant attention. These models can simulate complex and nonlinear relationships between data and are widely used in areas where accurate and comprehensive hydrological data is not available. In this study, the Long Short-Term Memory (LSTM) model was used to forecast groundwater levels in the Saadat Abad area of the Tashk-Bakhtegan Basin in Fars Province. The main objective of this study was to evaluate the performance of the LSTM model compared to traditional models and to analyze the impact of different activation functions on the accuracy of groundwater level forecasting. Bayesian optimization was employed to optimize the model's hyperparameters, which significantly improved the forecasting accuracy and the simulation of long-term dependencies between input data.The results of this study showed that the LSTM model is capable of forecasting groundwater level fluctuations and long-term trends with high accuracy. Additionally, a comparison of different activation functions revealed that the ReLU activation function with NSE value of 0.99, an R² value of 0.97, and an RMSE of 0.67 m, simulated the changes in groundwater levels. Furthermore, it was observed that using GPU significantly reduced processing time. Specifically, the execution time with CPU was 31 minutes, while with GPU it was only 9 minutes. This model demonstrated a high ability to simulate complex temporal patterns and accurately forecast groundwater levels, making it an efficient tool for groundwater resource management in regions with limited data.
Keywords
Bayesian optimization
Activation function
Simulation
Water resource management
Subjects
  1. Abadi, M., Barhum, P., & Mishra, A. K. (2017). A machine learning approach to radar sea clutter suppression. 2017 IEEE Radar Conference, RadarConf 2017, 1222-1227. https://doi.org/10.1109/RADAR.2017.7944391
  2. Abdi, E., Ali, M., Santos, C. A. G., Olusola, A., & Ghorbani, M. A. (2024). Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models. Groundwater for Sustainable Development, 26(June). https://doi.org/10.1016/j.gsd.2024.101213
  3. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181
  4. Benielli, D., Capponi, C., Koço, S., Kadri, H., Huusari, R., Bauvin, B., & Laviolette, F. (2022). Toolbox for Multimodal Learn (scikit-multimodallearn). Journal of Machine Learning Research, 23, 1-7.
  5. Bowes, B. D., Sadler, J. M., Morsy, M. M., Behl, M., & Goodall, J. L. (2019). Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks. Water (Switzerland), 11(5). https://doi.org/10.3390/w11051098
  6. Chen, Q., Ling, Z., Jiang, H., Zhu, X., Wei, S., & Inkpen, D. (2017). Enhanced LSTM for natural language inference. ACL 2017-55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 1(2008), 1657-1668. https://doi.org/10.18653/v1/P17-1152
  7. Chollet, F. (2015). No Title. Keras. https://github.com/keras-team/keras
  8. Feng, F., Ghorbani, H., & Radwan, A. E. (2024). Predicting groundwater level using traditional and deep machine learning algorithms. Frontiers in Environmental Science, 12(February). https://doi.org/10.3389/fenvs.2024.1291327
  9. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10), 2451-2471. https://doi.org/10.1162/089976600300015015
  10. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  11. Hock, R. (1999). A distributed temperature-index ice- and snowmelt model including potential direct solar radiation. Journal of Glaciology, 45(149), 101-111. https://doi.org/DOI: 10.3189/S0022143000003087
  12. Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., & Iguchi, T. (2014). The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95(5), 701-722. https://doi.org/10.1175/BAMS-D-13-00164.1
  13. Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
  14. Jeong, J., Park, E., Chen, H., Kim, K. Y., Shik Han, W., & Suk, H. (2020). Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data. Journal of Hydrology, 582(December 2019), 124512. https://doi.org/10.1016/j.jhydrol.2019.124512
  15. Lallahem, S., Mania, J., Hani, A., & Najjar, Y. (2005). On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology, 307(1), 92-111. https://doi.org/https://doi.org/10.1016/j.jhydrol.2004.10.005
  16. Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 226, 107161. https://doi.org/https://doi.org/10.1016/j.cmpb.2022.107161
  17. Masoumi, Z., Rezaei, A., & Maleki, J. (2019). Improvement of water table interpolation and groundwater storage volume using fuzzy computations. Environmental Monitoring and Assessment, 191(6), 1-15. https://doi.org/10.1007/s10661-019-7513-1
  18. McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 1(Scipy), 56-61. https://doi.org/10.25080/majora-92bf1922-00a
  19. Omar, P. J., Gaur, S., Dwivedi, S. B., & Dikshit, P. K. S. (2019). Groundwater modelling using an analytic element method and finite difference method: An insight into Lower Ganga river basin. Journal of Earth System Science, 128(7), 195. https://doi.org/10.1007/s12040-019-1225-3
  20. Ren, H., Cromwell, E., Kravitz, B., & Chen, X. (2022). Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks. Earth Syst. Sci., 26(7), 1727-1743. https://doi.org/10.5194/hess-26-1727-2022
  21. Shin, M. J., Moon, S. H., Kang, K. G., Moon, D. C., & Koh, H. J. (2020). Analysis of groundwater level variations caused by the changes in groundwater withdrawals using long short-term memory network. Hydrology, 7(3). https://doi.org/10.3390/HYDROLOGY7030064
  22. Sreekanth, P. D., Sreedevi, P. D., Ahmed, S., & Geethanjali, N. (2011). Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, 62(6), 1301-1310. https://doi.org/10.1007/s12665-010-0617-0
  23. Tan, M. L., & Duan, Z. (2017). Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing, 9(7), 1-16. https://doi.org/10.3390/rs9070720
  24. Van der Walt, S., & Aivazis, M. (2011). The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering. Computing in Science and Engineering, 13(2), 22-30. http://aip.scitation.org/doi/abs/10.1109/MCSE.2011.37
  25. Wang, Z., Zhong, R., Lai, C., & Chen, J. (2017). Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility. Atmospheric Research, 196(May), 151-163. https://doi.org/10.1016/j.atmosres.2017.06.020
  26. Zeydalinejad, N. (2022). Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: a review. Modeling Earth Systems and Environment, 8(3), 2911-2932. https://doi.org/10.1007/s40808-022-01365-y
  27. Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. https://doi.org/10.1016/j.jhydrol.2018.04.065
Water and Irrigation Management
Volume 15, Issue 2
Autumn 2025
Pages 279-299