Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer
coastal aquifer; convolutional neural network; hybrid neural network; hydraulic head prediction; surrogate model; Civil and Structural Engineering; Water Science and Technology; Geotechnical Engineering and Engineering Geology; Atmospheric Science
Abstract :
[en] This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model’s efficacy is confirmed through k-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model’s ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this machine learning model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.
Disciplines :
Civil engineering
Author, co-author :
Al-Maktoumi, Ali ; Water Research Center, Sultan Qaboos University, Muscat, Oman ; College of Agriculture and Marine Sciences, Sultan Qaboos University, Muscat, Oman
RAJABI, Mohammadmahdi ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; Civil and Environmental Engineering Faculty, Tarbiat Modares University, Tehran, Iran
Zekri, Slim; College of Agriculture and Marine Sciences, Sultan Qaboos University, Muscat, Oman
Govindan, Rajesh; College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Panjehfouladgaran, Aref; Civil and Environmental Engineering Faculty, Tarbiat Modares University, Tehran, Iran ; Department of Civil and Environmental Engineering, Western University, London, Canada
Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer
The authors would like to acknowledge the support of Hamed Bin Khalifa University-Qatar, and Sultan Qaboos University (SQU), Oman, for the support received through the awarded grants NPRP13S-0129-200198 (for SQU, EG/DVC/WRC/21/ 01). The support of the research group DR/RG/017 is also appreciated.
Ahmad, A. Y. & Al-Ghouti, M. A. 2020 Approaches to achieve sustainable use and management of groundwater resources in Qatar: A review. Groundwater for Sustainable Development 11, 100367.
Ajjur, S. B. & Al-Ghamdi, S. G. 2022 Quantifying the uncertainty in future groundwater recharge simulations from regional climate models. Hydrological Processes 36 (8), e14645.
Asadi, A. & Adhikari, K. 2022 Minimizing errors in the prediction of water levels using kriging technique in residuals of the groundwater model. Water 14 (3), 426.
Asher, M. J., Croke, B. F., Jakeman, A. J. & Peeters, L. J. 2015 A review of surrogate models and their application to groundwater modeling. Water Resources Research 51 (8), 5957–5973.
Azizpour, A., Izadbakhsh, M. A., Shabanlou, S., Yosefvand, F. & Rajabi, A. 2022 Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model. Environmental Science and Pollution Research 29 (19), 1–17.
Baalousha, H. M. 2016a Development of a groundwater flow model for the highly parameterized Qatar aquifers. Modeling Earth Systems and Environment 2, 1–11.
Baalousha, H. M. 2016b Groundwater vulnerability mapping of Qatar aquifers. Journal of African Earth Sciences 124, 75–93.
Baalousha, H. M. 2016c Using Monte Carlo simulation to estimate natural groundwater recharge in Qatar. Modeling Earth Systems and Environment 2, 1–7.
Baalousha, H., McPhee, H. M. & Anderson, M. J. 2015 Estimation of natural groundwater recharge in Qatar using GIS. In MODSIM2015, 21st International Congress on Modelling and Simulation, pp. 2026–2032.
Baalousha, H. M., Barth, N., Ramasomanana, F. H. & Ahzi, S. 2018 Groundwater recharge estimation and its spatial distribution in arid regions using GIS: A case study from Qatar karst aquifer. Modeling Earth Systems and Environment 4, 1319–1329.
Baalousha, H. M., Fahs, M., Ramasomanana, F. & Younes, A. 2019 Effect of pilot-points location on model calibration: Application to the northern karst aquifer of Qatar. Water 11 (4), 679.
Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn, J. J. & Fienen, M. N. 2016 Scripting MODFLOW model development using Python and FloPy. Groundwater 54 (5), 733–739.
Celik, R. & Aslan, V. 2020 Evaluation of hydrological and hydrogeological characteristics affecting the groundwater potential of Harran Basin. Arabian Journal of Geosciences 13, 1–13.
Chen, Y., Liu, G., Huang, X., Chen, K., Hou, J. & Zhou, J. 2021 Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis. Journal of Hydrology 598, 125726.
Chen, Y., Li, L., Li, W., Guo, Q., Du, Z. & Xu, Z. 2022 AI Computing Systems: An Application Driven Perspective. Elsevier.
Cheng, M. Y. & Cao, M. T. 2014 Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Applied Soft Computing 22, 178–188.
Christelis, V., Kopsiaftis, G. & Mantoglou, A. 2019 Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers. Hydrological Sciences Journal 64 (3), 336–349.
Clifton, P. M. & Neuman, S. P. 1982 Effects of kriging and inverse modeling on conditional simulation of the Avra Valley aquifer in southern Arizona. Water Resources Research 18 (4), 1215–1234.
Du, B., Zhou, Q., Guo, J., Guo, S. & Wang, L. 2021 Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting. Expert Systems with Applications 171, 114571.
Eccleston, B. L., Pike, J. G. & Harhash, I. 1981 The Water Resources of Qatar and Their Development, Vol. I. Food and Agricultural Organization of the United Nations, Roime, Italy.
Eslami, P., Nasirian, A., Akbarpour, A. & Nazeri Tahroudi, M. 2022 Groundwater estimation of Ghayen plain with regression-based and hybrid time series models. Paddy and Water Environment 20 (3), 429–440.
Gadd, C., Xing, W., Nezhad, M. M. & Shah, A. A. 2019 A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models. Transport in Porous Media 126, 39–77.
Garre, A., Ruiz, M. C. & Hontoria, E. 2020 Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty. Operations Research Perspectives 7, 100147.
Gong, Y., Wang, Z., Xu, G. & Zhang, Z. 2018 A comparative study of groundwater level forecasting using data-driven models based on ensemble empirical mode decomposition. Water 10 (6), 730.
Guo, Q., Zhao, Y., Hu, Z. & Li, M. 2021 Contamination transport in the coastal unconfined aquifer under the influences of seawater intrusion and inland freshwater recharge – Laboratory experiments and numerical simulations. International Journal of Environmental Research and Public Health 18 (2), 762.
Han, Z., Lu, W., Fan, Y., Lin, J. & Yuan, Q. 2020 A surrogate-based simulation–optimization approach for coastal aquifer management. Water Supply 20 (8), 3404–3418.
Jiang, Z., Tahmasebi, P. & Mao, Z. 2021 Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems. Advances in Water Resources 150, 103878.
Khan, M. A., Zafar, A., Farooq, F., Javed, M. F., Alyousef, R., Alabduljabbar, H. & Khan, M. I. 2021 Geopolymer concrete compressive strength via artificial neural network, adaptive neuro fuzzy interface system, and gene expression programming with K-fold cross validation. Frontiers in Materials 8, 621163.
Kontos, Y. N., Kassandros, T., Perifanos, K., Karampasis, M., Katsifarakis, K. L. & Karatzas, K. 2022 Machine learning for groundwater pollution source identification and monitoring network optimization. Neural Computing and Applications 34 (22), 19515–19545.
Kopsiaftis, G., Protopapadakis, E., Voulodimos, A., Doulamis, N. & Mantoglou, A. 2019 Gaussian process regression tuned by bayesian optimization for seawater intrusion prediction. Computational intelligence and neuroscience, 2019, 2859429.
Kouziokas, G. N., Chatzigeorgiou, A. & Perakis, K. 2018 Multilayer feed forward models in groundwater level forecasting using meteorological data in public management. Water Resources Management 32, 5041–5052.
LaHaye, O., Habib, E. H., Vahdat-Aboueshagh, H., Tsai, F. T. C. & Borrok, D. 2021 Assessment of aquifer storage and recovery feasibility using numerical modeling and geospatial analysis: application in Louisiana. JAWRA Journal of the American Water Resources Association 57 (3), 505–526.
Lal, A. & Datta, B. 2018 Development and implementation of support vector machine regression surrogate models for predicting groundwater pumping-induced saltwater intrusion into coastal aquifers. Water Resources Management 32, 2405–2419.
Laloy, E., Rogiers, B., Vrugt, J. A., Mallants, D. & Jacques, D. 2013 Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion. Water Resources Research 49 (5), 2664–2682.
Li, Q., Wang, Z., Shangguan, W., Li, L., Yao, Y. & Yu, F. 2021 Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning. Journal of Hydrology 600, 126698.
Liu, Q., Gui, D., Zhang, L., Niu, J., Dai, H., Wei, G. & Hu, B. X. 2022 Simulation of regional groundwater levels in arid regions using interpretable machine learning models. Science of The Total Environment 831, 154902.
Miao, T., Lu, W., Lin, J., Guo, J. & Liu, T. 2019 Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: A case study in Longkou, China. Arabian Journal of Geosciences 12, 1–11.
Mirarabi, A., Nassery, H. R., Nakhaei, M., Adamowski, J., Akbarzadeh, A. H. & Alijani, F. 2019 Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environmental Earth Sciences 78, 1–15.
Miro, M. E., Groves, D., Tincher, B., Syme, J., Tanverakul, S. & Catt, D. 2021 Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making. Climate Risk Management 34, 100383.
Mo, S., Zabaras, N., Shi, X. & Wu, J. 2019 Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. Water Resources Research 55 (5), 3856–3881.
Mo, S., Zabaras, N., Shi, X. & Wu, J. 2020 Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities. Water Resources Research 56 (2), e2019WR026082.
Najafzadeh, M., Homaei, F. & Mohamadi, S. 2022 Reliability evaluation of groundwater quality index using data-driven models. Environmental Science and Pollution Research 29 (6), 8174–8190.
Nguyen, X. C., Nguyen, T. T. H., La, D. D., Kumar, G., Rene, E. R., Nguyen, D. D., Chang, S. W., Chung, W. J., Nguyen, X. H. & Nguyen, V. K. 2021 Development of machine learning-based models to forecast solid waste generation in residential areas: A case study from Vietnam. Resources, Conservation and Recycling 167, 105381.
Nourani, V., Mousavi, S., Dabrowska, D. & Sadikoglu, F. 2017 Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media. Journal of Hydrology 548, 569–587.
Omar, P. J., Gaur, S., Dwivedi, S. B. & Dikshit, P. K. S. 2020 A modular three-dimensional scenario-based numerical modelling of groundwater flow. Water Resources Management 34, 1913–1932.
Ostad-Ali-Askari, K. & Shayannejad, M. 2021 Quantity and quality modelling of groundwater to manage water resources in Isfahan-Borkhar Aquifer. Environment, Development and Sustainability 23 (11), 15943–15959.
Pan, Z., Lu, W., Wang, H. & Bai, Y. 2022 Fast inverse estimation of hydraulic conductivity field based on a deep convolutional-cycle generative adversarial neural network. Journal of Hydrology 613, 128420.
Panjehfouladgaran, A. & Rajabi, M. M. 2022 Contaminant source characterization in a coastal aquifer influenced by tidal forces and density-driven flow. Journal of Hydrology 610, 127807.
Payne, K., Chami, P., Odle, I., Yawson, D. O., Paul, J., Maharaj-Jagdip, A. & Cashman, A. 2022 Machine learning for surrogate groundwater modelling of a small carbonate island. Hydrology 10 (1), 2.
Rajabi, M. M. 2019 Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: Polynomial chaos expansion and Gaussian process emulation. Stochastic Environmental Research and Risk Assessment 33, 607–631.
Rajabi, M. M., Javaran, M. R. H., Bah, A. O., Frey, G., Le Ber, F., Lehmann, F. & Fahs, M. 2022 Analyzing the efficiency and robustness of deep convolutional neural networks for modeling natural convection in heterogeneous porous media. International Journal of Heat and Mass Transfer 183, 122131.
Rizeei, H. M., Azeez, O. S., Pradhan, B. & Khamees, H. H. 2018 Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models. Environmental Monitoring and Assessment 190, 1–17.
Robinson, C., Brovelli, A., Barry, D. A. & Li, L. 2009 Tidal influence on BTEX biodegradation in sandy coastal aquifers. Advances in Water Resources 32 (1), 16–28.
Sarma, R. & Singh, S. K. 2022 A comparative study of data-driven models for groundwater level forecasting. Water Resources Management 36 (8), 2741–2756.
Saud, S., Jamil, B., Upadhyay, Y. & Irshad, K. 2020 Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach. Sustainable Energy Technologies and Assessments 40, 100768.
Schlumberger Water Services 2009 Studying and Developing the Natural and Artificial Recharge of the Groundwater in Aquifer in the State of Qatar, Appendices; Schlumberger Water Services, Doha, Qatar. Project final report retrieved from Department of Agricultural and Water Research (DAWR), Ministry of Environment (MoE).
Shakeri, R., Nassery, H. R. & Ebadi, T. 2023 Numerical modeling of groundwater flow and nitrate transport using MODFLOW and MT3DMS in the Karaj alluvial aquifer, Iran. Environmental Monitoring and Assessment 195 (1), 242.
Shivakoti, B. R., Villholth, K. G., Pavelic, P. & Ross, A. 2019 Strategic use of groundwater-based solutions for drought risk reduction and climate resilience in Asia and beyond. Contributing paper to Global Assessment Report on disaster risk reduction (GAR 2019). United Nations Office for Disaster Risk Reduction, Geneva, Switzerland.
Sinitsin, V., Ibryaeva, O., Sakovskaya, V. & Eremeeva, V. 2022 Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model. Mechanical Systems and Signal Processing 180, 109454.
Sreekanth, J. & Datta, B. 2011 Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization. Water Resources Research 47 (4), 1–17.
Sun, Q., Gao, M., Wen, Z., Guo, F., Hou, G., Liu, Z., Cai, Z., Chang, X., Zheng, T. & Zhao, G. 2023 Reactive transport modeling for the effect of pumping activities on the groundwater environment in muddy coasts. Journal of Hydrology 621, 129614.
Taccari, M. L., Nuttall, J., Chen, X., Wang, H., Minnema, B. & Jimack, P. K. 2022 Attention U-Net as a surrogate model for groundwater prediction. Advances in Water Resources 163, 104169.
Taccari, M. L., Wang, H., Goswami, S., Nuttall, J., Chen, X. & Jimack, P. K. 2023 Developing a cost-effective emulator for groundwater flow modeling using deep neural operators. arXiv preprint arXiv:2304.12299.
Tian, J., Li, C., Liu, J., Yu, F., Cheng, S., Zhao, N. & Wan Jaafar, W. Z. 2016 Groundwater depth prediction using data-driven models with the assistance of gamma test. Sustainability 8 (11), 1076.
Tiwari, S., Yadav, B. K. & Polemio, M. 2022 Performance evaluation of Aquifer Storage and Recovery (ASR) system in saline groundwater regions: Impact of operational factors and well design. Journal of Hydrologic Engineering 27 (12), 04022032.
Tiwari, S., Tripathi, D. & Yadav, B. K. 2023 Enhanced performance of aquifer storage and recovery system in unconfined saline aquifer for different operational factors: A multidimensional laboratory and modeling study. Journal of Water Resources Planning and Management 149 (8), 04023033.
Vu, H. L., Ng, K. T. W., Richter, A. & An, C. 2022 Analysis of input set characteristics and variances on k-fold cross validation for a recurrent neural network model on waste disposal rate estimation. Journal of Environmental Management 311, 114869.
Wang, X., Sun, Y., Xu, Z., Zheng, J. & Zhang, C. 2020 Feasibility prediction analysis of groundwater reservoir construction based on GMS and Monte Carlo analyses: A case study from the Dadougou Coal Mine, Shanxi Province, China. Arabian Journal of Geosciences 13, 1–11.
Welter, D. E., White, J. T., Hunt, R. J. & Doherty, J. E. 2015 Approaches in highly parameterized inversion – PEST þþ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models (No. 7-C12). US Geological Survey.
Wossenyeleh, B. K., Worku, K. A., Verbeiren, B. & Huysmans, M. 2021 Drought propagation and its impact on groundwater hydrology of wetlands: A case study on the Doode Bemde nature reserve (Belgium). Natural Hazards and Earth System Sciences 21 (1), 39–51.
Xia, X., Jiang, S., Zhou, N., Cui, J. & Li, X. 2023 Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network. Journal of Hydrology 617, 129013.
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O. & Lee, K. K. 2011 A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396 (1–2), 128–138.
Zheng, N., Jiang, S., Xia, X., Kong, W., Li, Z., Gu, S. & Wu, Z. 2023 Efficient estimation of groundwater contaminant source and hydraulic conductivity by an ILUES framework combining GAN and CNN. Journal of Hydrology 621, 129677.
Zhou, Y. & Li, W. 2011 A review of regional groundwater flow modeling. Geoscience Frontiers 2 (2), 205–214.
Zhou, Z. & Tartakovsky, D. M. 2021 Markov chain Monte Carlo with neural network surrogates: Application to contaminant source identification. Stochastic Environmental Research and Risk Assessment 35, 639–651.
Zhu, Y. & Zabaras, N. 2018 Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification. Journal of Computational Physics 366, 415–447.
