Predicting saltwater concentration in coastal aquifers using hybrid models
Document Type : Research Paper
Authors
- 1 Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
- 2 Asoc. Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran
- 3 Assist. Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Abstract
This study presents an integrated approach that combines laboratory experiments, numerical modeling, and machine learning to enhance salinity prediction in coastal aquifers. A laboratory model simulated saltwater intrusion under controlled conditions, systematically varying water density (25, 35, 50 g/l), extraction rate (0.05, 0.12, 0.25 l/min), and extraction depth (12, 25, 36 cm) to examine hydrodynamic and geochemical interactions. Experimental data calibrated the SEAWAT numerical model, which generated additional scenarios for machine learning analysis. The study developed hybrid predictive models combining Support Vector Regression and Random Forest with Convolutional Neural Networks, optimized using the Golden Ball Optimization algorithm. Key input parameters, including relative water density, extraction rate, and well depth, were evaluated through a comprehensive statistical analysis. The evaluation results indicated that the RF-GBO-3 model, with a root mean square error (RMSE) of 0.039, exhibited the best performance among the models, while the SVR-GBO-3 model, with an RMSE of 0.056, also showed satisfactory performance. The Golden Ball Optimization algorithm enhanced model performance by effectively tuning critical parameters and capturing complex nonlinear relationships. These findings advance saltwater intrusion modeling by providing a robust framework that integrates physical experiments with data-driven techniques, offering improved tools for coastal water resource management.
Keywords
Subjects
environment
Abd-Elaty, I., Abd-Elhamid, H. F., & Qahman, K. (2020). Coastal aquifer protection from saltwater intrusion using abstraction of brackish water and recharge of treated wastewater: case study of the Gaza aquifer. Journal of Hydrologic Engineering, 25(7), 200-218. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001927.
Abd-Elaty, I., Sallam, G. A., Straface, S., & Scozzari, A. (2019). Effects of climate change on the design of subsurface drainage systems in coastal aquifers in arid/semi-arid regions: Case study of the Nile delta. Science of the Total Environment, 672(1), 283-295. https://doi.org/10.1016/j.scitotenv.2019.03.483.
Abd-Elaty, I., Straface, S., & Kuriqi, A. (2021). Sustainable saltwater intrusion management in coastal aquifers under climatic changes for humid and hyper-arid regions. Ecological Engineering, 171(14), 106-121. https://doi.org/10.1016/j.ecoleng.2021.106382.
Abd-Elhamid, H., Javadi, A., Abdelaty, I., & Sherif, M. (2016). Simulation of seawater intrusion in the Nile Delta aquifer under the conditions of climate change. Hydrology Research. 47 (6), 1198-1210. https://doi.org/10.2166/nh.2016.157.
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Umar, A. M., Linus, O. U., Arshad, H., Kazaure, A. A., Gana, U. & Kiru, M. U. (2019). Comprehensive review of artificial neural network applications to pattern recognition. IEEE 7 (2), (pp.158820-158846). 10.1109/ACCESS.2019.2891234.
Abuein, Q. Q., Shatnawi, M. Q., Aljawarneh, E. Y. & Manasrah, A. (2024). Time Series Forecasting Model for the Stock Market using LSTM and SVR. International Journal of Advances in Soft Computing and its Applications. 16 (1), 17-28. 10.15849/IJASCA.240330.10.
Bakır, R., Orak, C. & Yüksel, A. (2024). Optimizing hydrogen evolution prediction: A unified approach using random forests, lightGBM, and Bagging Regressor ensemble model. International Journal of Hydrogen Energy. 67 (2), 101-110. https://doi.org/10.1016/j.ijhydene.2024.04.173.
Banaduc, D., Simić, V., Cianfaglione, K., Barinova, S., Afanasyev, S., Öktener, A., McCall, G., Simić, S. & Curtean-Bănăduc, A. (2022). Freshwater as a sustainable resource and generator of secondary resources in the 21st century: Stressors, threats, risks, management and protection strategies, and conservation approaches. International Journal of Environmental Research and Public Health. 19 (24), 16570-16584. https://doi.org/10.3390/ijerph192416570.
Basack, S., Bhattacharya, A. & Maity, P. (2012) Hydrogeological investigation on saline water intrusion into a coastal aquifer of West Bengal, India. Proceedings of Indian Geotechnical Conference.
Basak, D., Pal, S. & Patranabis, D. C. (2007). Support vector regression. Neural Processing Letters. 11 (10), 203-224. https://doi.org/10.4314/niplr.v11i10.37063.
Breiman, L. (2001). Random forests. Machine Learning. 45 (1), 5-32. https://doi.org/10.1023/A:1010933404324.
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances in Neural Information Processing Systems. 9 (1).
Faal, F., Ghafouri, H. R. & Ashrafi, S. M. (2020). Predicting Saltwater Intrusion into Coastal Aquifers Using Support Vector Regression Surrogate Models. Water and Wastewater 31 (126), 118-129. (In Persian with English abstract).https://doi.org/10.22093/wwj.2019.161020.2803.
Fawagreh, K., Gaber, M. M. & Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering. 2 (1), 602-609. https://doi.org/10.1080/21681163.2014.981123.
Gopinath, S., Srinivasamoorthy, K., Saravanan, K., Suma, C., Prakash, R., Senthilnathan, D., Chandrasekaran, N., Srinivas, Y. & Sarma, V. (2016). Modeling saline water intrusion in Nagapattinam coastal aquifers, Tamilnadu, India. Modeling Earth Systems and Environment. 2 (1), 1-10. https://doi.org/10.1007/s40808-015-0058-6.
Guo, H. & Jiao, J. J. (2007). Impact of coastal land reclamation on ground water level and the sea water interface. Groundwater 45 (3), 362-367. https://doi.org/10.1111/j.1745-6584.2006.00290.x.
Karami Moghadam, M., Amini, A., & Keshavarzi, A. (2020). Intake design attributes and submerged vanes effects on sedimentation and shear stress. Water and Environment Journal, 34(4), 658–670. https://doi.org/10.1111/wej.12471.
Klassen, J. and Allen, D. (2017). Assessing the risk of saltwater intrusion in coastal aquifers. J. Hydrol. 551 (12), 730-745. https://doi.org/10.1016/j.jhydrol.2017.02.044.
Kolathayar, S., Sitharam, T. & Yang, S. (2019). Coastal reservoir strategy to enhance India's freshwater storage by impounding river flood waters: a detailed overview. Water Supply 19 (3), 703-717. https://doi.org/10.2166/ws.2018.140.
LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), (pp.191-200).
Naseer, A., Al Mudawi, N., Abdelhaq, M., Alonazi, M., Alazaib, A., Algarni, A. & Jalal, A. (2024). CNN-based Object Detection via Segmentation capabilities in Outdoor Natural Scenes. IEEE 17 (1), 25-32. https://doi.org/10.1109/ACCESS.2024.3413848.
Osaba, E., Diaz, F. & Onieva, E. (2014). Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence. 41 (1), 145-166. https://doi.org/10.1007/s10489-013-0512-y.
Ranjbar, A. & Ehteshami, M. (2019). Spatio-temporal mapping of salinity in the heterogeneous coastal aquifer. Applied Water Science. 9 (2), 32. https://doi.org/10.1007/s13201-019-0908-x.
Ravindiran, G., Rajamanickam, S., Sivarethinamohan, S., Karupaiya Sathaiah, B., Ravindran, G., Muniasamy, S. K. & Hayder, G. (2023). A Review of the Status, Effects, Prevention, and Remediation of Groundwater Contamination for Sustainable Environment. Water 15 (20), 3662-3674. https://doi.org/10.3390/w15203662.
Roy, D. K., Paul, C. R., Munmun, T. H., & Datta, B. (2024). An automatic model selection-based machine learning approach to predict seawater intrusion into coastal aquifers. Environmental Earth Sciences. 83(9), 287-301. https://doi.org/10.1007/s12665-024-11589-z
Sharafi, M., Rezaverdinejad, V., Behmanesh, J. & Samadianfard, S. (2024). Development of long short-term memory along with differential optimization and neural networks for coagulant dosage prediction in water treatment plant. Journal of Water Process Engineering. 65 (14), 5784-5796. https://doi.org/10.1016/j.jwpe.2024.105784.
Sharafi, M. & Samadianfard, S. (2024). Prediction of daily evaporation values using support vector regression coupled with firefly algorithm in the coastal areas of Iran. Water and Soil Science 34 (1), 39-53. (In Persian with English abstract). https://doi.org/10.22034/ws.2023.54178.2500.
Srilakshmi, K., Sundaragiri, D., Gaddameedhi, S., Vangalapudi, R., Balachandran, P. K., Colak, I. & Selvarajan, S. (2024). Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm. Frontiers in Energy Research. 12 (2), 1370-1384. https://doi.org/10.3389/fenrg.2024.1370412.
Suganya, D., Thirunadana Sikamani, K. & Sasikala, J. (2022). Copy-move forgery detection of medical images using golden ball optimization. International Journal of Computer Applications. 44 (8), 729-737. https://doi.org/10.1080/1206212X.2021.1907905.
Tanveer, M., Rajani, T., Rastogi, R., Shao, Y.-H. & Ganaie, M. (2024). Comprehensive review on twin support vector machines. Annals of Operations Research. 339 (3), 1223-1268. https://doi.org/10.1007/s10479-022-04575-w.
Taye, M. M. (2023). Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation. 11 (3), 52-64. https://doi.org/10.3390/computation11030052.
Todd, D. K. (1974). Salt‐water intrusion and its control. Journal - American Water Works Association. 66 (3), 180-187. https://doi.org/10.1002/j.1551-8833.1974.tb01999.x.
Tran, D. A., Tsujimura, M., Ha, N. T., Van Binh, D., Dang, T. D., Doan, Q.-V., Bui, D. T., Ngoc, T. A., Thuc, P. T. B. & Pham, T. D. (2021). Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators. 127(1), 107-128. https://doi.org/10.1016/j.ecolind.2021.107790.
Wang, N., and Ge, J. (2025). Predictions of saltwater intrusion in the Changjiang Estuary: Integrating Machine learning methods with FVCOM. Journal of Hydrology. 12(1), 132-145. https://doi.org/10.1016/j.jhydrol.2025.132739
Werner, A. D. & Simmons, C. T. (2009). Impact of sea‐level rise on sea water intrusion in coastal aquifers. Groundwater 47 (2), 197-204. https://doi.org/10.1111/j.1745-6584.2008.00535.x.
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