Modeling of daily groundwater level using deep learning neural networks

Groundwater is an essential water source, becoming more vital due to shortages in available surface water resources. Hence, monitoring groundwater levels can show the amount of water available to extract and use for various purposes. However, the groundwater system is naturally complex, and we need models to simulate it. Therefore, we employed a deep learning model called CNN-biLSTM neural networks for modeling groundwater, and the data was obtained from USGS. The data included daily groundwater levels from 2002 to 2021, and the data was divided into 95% for training and 5% for testing. Besides, three deep CNN-biLSTM models were employed using three different algorithms (SGDM, ADAM, and RMSprop(. Also, Bayesian optimization was used to optimize parameters such as the number of biLSTM layers and the number of biLSTM units. The model's performance was based on Spearman's Rank-Order Correlation (r), and the model with SGDM showed the best results compared to other models in this study. Finally, the CNN model with LSTM can simulate time series data effectively.

Keywords:

Artificial Intelligence, CNN-biLSTM, Groundwater Neural Network,

PDF

___

Ao, C., Zeng, W., Wu, L., Qian, L., Srivastava, A. K., & Gaiser, T. (2021). Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China. Agricultural Water Management, 255, 107032.
Taylor, C. J., & Alley, W. M. (2001). Ground-water-level monitoring and the importance of long-term water-level data (Vol. 1217). Denver, CO, USA: US Geological Survey.
Wunsch, A., Liesch, T., & Broda, S. (2020). Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX. Hydrology and Earth System Sciences Discussions, 2020, 1-23.
Ebrahimi, S., & Khorram, M. (2021). Variability effect of hydrological regime on river quality pattern and its uncertainties: case study of Zarjoob River in Iran. Journal of Hydroinformatics, 23(5), 1146-1164.
Thangarajan, M. (2007). Groundwater models and their role in assessment and management of groundwater resources and pollution. In groundwater (pp. 189-236). Springer, Dordrecht.
Bear, J., Beljin, M. S., & Ross, R. R. (1992). Fundamentals of groundwater modeling. Ground-water issue (No. PB-92-232354/XAB; EPA-540/S-92/005). Environmental Protection Agency, Ada, OK (United States). Robert S. Kerr Environmental Research Lab.
Anderson, M. P., Woessner, W. W., & Hunt, R. J. (2015). Introduction. Applied Groundwater Modeling, 3–25. https://doi.org/10.1016/b978-0-08-091638-5.00001-8
Alasta, M. S., Ali, A. S. A., Ebrahimi, S., Ashiq, M. M., Dheyab, A. S., AlMasri, A., Alqatanani, A., & Khorram, M. Modeling of Local Scour Depth Around Bridge Pier Using FLOW 3D.
Prickett, T. A. (1975). Modeling techniques for groundwater evaluation. In Advances in hydroscience (Vol. 10, pp. 1-143). Elsevier.
Faulkner, J., Hu, B. X., Kish, S., & Hua, F. (2009). Laboratory analog and numerical study of groundwater flow and solute transport in a karst aquifer with conduit and matrix domains. Journal of contaminant hydrology, 110(1-2), 34-44.
Gholami, V. C. K. W., Chau, K. W., Fadaee, F., Torkaman, J., & Ghaffari, A. (2015). Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. Journal of hydrology, 529, 1060-1069.
Boyraz, U., & Kazezyılmaz-Alhan, C. M. (2018). Solutions for groundwater flow with sloping stream boundary: analytical, numerical and experimental models. Hydrology Research, 49(4), 1120-1130.
Lee, W. D., Yoo, Y. J., Jeong, Y. M., & Hur, D. S. (2019). Experimental and numerical analysis on hydraulic characteristics of coastal aquifers with seawall. Water, 11(11), 2343.
Xu, Y. S., Yan, X. X., Shen, S. L., & Zhou, A. N. (2019). Experimental investigation on the blocking of groundwater seepage from a waterproof curtain during pumped dewatering in an excavation. Hydrogeology Journal, 27(7), 2659-2672.
Kagabu, M., Ide, K., Hosono, T., Nakagawa, K., & Shimada, J. (2020). Describing coseismic groundwater level rise using tank model in volcanic aquifers, Kumamoto, southern Japan. Journal of Hydrology, 582, 124464.
Ansarifar, M. M., Salarijazi, M., Ghorbani, K., & Kaboli, A. R. (2020). Simulation of groundwater level in a coastal aquifer. Marine Georesources & Geotechnology, 38(3), 257-265.
Akter, A., & Ahmed, S. (2021). Modeling of groundwater level changes in an urban area. Sustainable Water Resources Management, 7(1), 1-2018.
Armanuos, A., Ahmed, K., Shiru, M. S., & Jamei, M. (2021). Impact of Increasing Pumping Discharge on Groundwater Level in the Nile Delta Aquifer, Egypt. Knowledge-Based Engineering and Sciences, 2(2), 13-23.
Yang, M., Liu, H., & Meng, W. (2021). An analytical solution of the tide-induced groundwater table overheight under a three-dimensional kinematic boundary condition. Journal of Hydrology, 595, 125986.
Melesse, A. M., & Hanley, R. S. (2005). Artificial neural network application for multi-ecosystem carbon flux simulation. Ecological Modelling, 189(3-4), 305-314.
Ali, A. S. A., & Günal, M. (2021). Artificial neural network for estimation of local scour depth around bridge piers. Archives of Hydro-Engineering and Environmental Mechanics, 68(2), 87-101.
Pérez-Pérez, E. J., López-Estrada, F. R., Valencia-Palomo, G., Torres, L., Puig, V., & Mina-Antonio, J. D. (2021). Leak diagnosis in pipelines using a combined artificial neural network approach. Control Engineering Practice, 107, 104677.
Pan, L., Novák, L., Lehký, D., Novák, D., & Cao, M. (2021). Neural network ensemble-based sensitivity analysis in structural engineering: Comparison of selected methods and the influence of statistical correlation. Computers & Structures, 242, 106376.
Wu, D., & Wang, G. G. (2021). Causal artificial neural network and its applications in engineering design. Engineering Applications of Artificial Intelligence, 97, 104089.
Azari, B., Hassan, K., Pierce, J., & Ebrahimi, S. (2022). Evaluation of machine learning methods application in temperature prediction. Transactions of Civil and Environmental Engineering, 8, 1-12.
Kashani, A. R., Camp, C. V., Akhani, M., & Ebrahimi, S. (2022). Optimum design of combined footings using swarm intelligence-based algorithms. Advances in Engineering Software, 169, 103140.
ALI, A. S. A. (2021). Republic of Turkey Gaziantep Unıversity Graduate School of Natural & Applied Sciences.
Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning (Vol. 1). Massachusetts, USA: MIT press.
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of Computational Chemistry, 38(16), 1291–1307. https://doi.org/10.1002/jcc.24764.
Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82.
Shreyas, N., Venkatraman, M., Malini, S., & Chandrakala, S. (2020). Trends of sound event recognition in audio surveillance: a recent review and study. The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems, 95-106.
Shen, C. (2018). A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Research, 54(11), 8558-8593.
Wunsch, A., Liesch, T., & Broda, S. (2021). Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrology and Earth System Sciences, 25(3), 1671-1687.
Rajaee, T., Ebrahimi, H., & Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of hydrology, 572, 336-351.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j. neunet.2014.09.003.
Samudrala, S. (2019). Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning. Notion Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019). Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica, 67(5), 1471-1481.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., & Dadson, S. J. (2021). Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: A comparison of LSTM-based models with four lumped conceptual models. Hydrology and Earth System Sciences.
Ayzel, G., Kurochkina, L., Abramov, D., & Zhuravlev, S. (2021). Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks. Hydrology, 8(1), 6.
Heindel, L., Hantschke, P., & Kästner, M. (2021). A Virtual Sensing approach for approximating nonlinear dynamical systems using LSTM networks. PAMM, 21(1), e202100119.
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.
Huang, X., Gao, L., Crosbie, R. S., Zhang, N., Fu, G., & Doble, R. (2019). Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning. Water, 11(9), 1879.
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), 64.
Vu, M. T., Jardani, A., Massei, N., & Fournier, M. (2021). Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network. Journal of Hydrology, 597, 125776.
Solgi, R., Loaiciga, H. A., & Kram, M. (2021). Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations. Journal of Hydrology, 601, 126800.
Yokoo, K., Ishida, K., Nagasato, T., Kawagoshi, Y., & Ito, H. (2021, October). Reconstruction of groundwater level at Kumamoto, Japan by means of deep learning to evaluate its increase by the 2016 earthquake. In IOP Conference Series: Earth and Environmental Science (Vol. 851, No. 1, p. 012032). IOP Publishing
Ali, A. S. A., Ebrahimi, S., Ashiq, M. M., Alasta, M. S., & Azari, B. (2022). CNN-Bi LSTM neural network for simulating groundwater level. CRPASE: Transactions of Civil and Environmental Engineering, 8, 1-7.
Guo, X. (2020, November). Prediction of taxi demand based on CNN-BiLSTM-attention neural network. In International Conference on Neural Information Processing (pp. 331-342). Springer, Cham.
Tao, Y., Sun, H., & Cai, Y. (2022). Predictions of Deep Excavation Responses Considering Model Uncertainty: Integrating BiLSTM Neural Networks with Bayesian Updating. International Journal of Geomechanics, 22(1), 04021250.
Dey, S., Dey, A. K., & Mall, R. K. (2021). Modeling long-term groundwater levels by exploring deep bidirectional long short-term memory using hydro-climatic data. Water Resources Management, 35(10), 3395-3410.
Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753.
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2015). Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2625-2634).
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28.
Anderson, S., & Radic, V. (2021). Evaluation and interpretation of convolutional-recurrent networks for regional hydrological modelling. Hydrology and Earth System Sciences. https://doi. org/10.5194/hess-2021-113, in review.
Yerima, S. Y., Alzaylaee, M. K., & Shajan, A. (2021). Deep learning techniques for android botnet detection. Electronics, 10(4), 519
Azizi, K., Kashani, A. R., Ebrahimi, S., & Jazaei, F. (2022). Application of a multi-objective optimization model for the design of piano key weirs with a fixed dam height. Canadian Journal of Civil Engineering, (ja).
Ashiq, M. M., Jazaei, F., Ali, A. S., & Bakhshaee, A. (2022, December). Investigation and Identification of the Microplastics Presence in the Soil. In Fall Meeting 2022. AGU.