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Assessment of Groundwater Hardness in Khorramabad Plain Using Hybrid Models Based on Metaheuristic Algorithms

Document Type : Original Article

Authors

1 Associate Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Khorramabad, Iran

2 Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran.

3 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization,

10.22077/jaaq.2025.8579.1083

Abstract

In this study, a hybrid Support Vector Regression (SVR) model, combined with optimization algorithms including Wavelet, Firefly, and Grey Wolf, was employed to estimate groundwater hardness at the Naservand well in the Khorramabad Plain of Lorestan Province. For modeling, monthly quality data of the well during the water year (1382-1402) were used. This included parameters such as bicarbonate (HCO3), chloride (Cl), sulfate (SO4), magnesium (Mg), calcium (Ca), and total hardness (TH), all measured in ppm, which were used as inputs. Groundwater hardness was selected as the model output. To evaluate the performance of the models, assessment metrics such as the correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) were used. Time series, box plots, and Taylor diagrams were also used for analyzing the model results. The results indicated that hybrid scenarios in the studied models improve the model’s performance. Comparing results showed that the Support Vector Regression-Wavelet model performed better than the Support Vector Regression-Grey Wolf model, with the Support Vector Regression-Wavelet model achieving a correlation coefficient of 0.917, the lowest RMSE of 0.190 ppm, the lowest MAE of 0.115 ppm, and the highest NSE of 0.920 in the validation stage. Overall, the results indicate that using intelligent models based on the Support Vector Regression approach can be an effective approach for managing groundwater quality.

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References
Chandel, N., Gupta, S., Ravi, A. (2024).Ground Water Quality Analysis using Machine Learning Techniques: a Critical Appraisal. Journal of Mining and Environment (JME), 15(2),  419-426.https://doi.org/10.22044/jme.2023.13452.2484
Chawla, V.K., Chanda, A.K., Angra, S. (2019). The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm. Journal of Project Management, 4(1):19-30.10.5267/j.jpm.2018.8.001
Dehghani, R., Babaali, H.(2023). Evaluation of Statistical Models and Modern Hybrid Artificial Intelligence in Simulation of Runoff Precipitation Process. Sustain. Water Resour. Manag, 8, 154-172. https://doi.org/10.1007/s40899-022-00743-9.
Dehghani, R., Torabi Poudeh, H., Younesi, H., Shahinejad, B.(2020). Daily Streamflow Prediction Using Support Vector Machine-Artificial Flora (SVM-AF) Hybrid Model. Acta Geophysica, 68(6), 51-66. https://doi.org/10.1007/s11600-020-00472-7
Dehghani, R., Torabi, H.(2021). Dissolved oxygen concentration predictions for running waters using hybrid machine learning techniques. Modeling Earth Systems and Environment, 6(2), 64-78. https://doi.org/10.1007/s40808-021-01253-x.
Dwivedi, S. L., & Pathak, V. (2007). A preliminary assignment of water quality index to Mandakini River, Chitrakoot. Indian Journal of Environmental Protection, 27(11), 103-122.10.4236/jwarp.2011.36050.
Emami, S., Emami, H., Choopan, Y., Jahandideh, O.(2021). Modeling groundwater quality using three novel hybrid support vector regression models. Advances in Environmental Technology .2 (4),99-110.10.22104/aet.2021.4286.1212
Gupta, S. K., Singh, P. K., Tyagi, J., Sharma, G., & Jethoo, A. S. (2020). Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP). Hydrological Processes, 34(16), 3448-3463. https://doi.org/10.1002/hyp.13789.
Gupta, S. K., Tyagi, J., Sharma, G., Jethoo, A. S., & Singh, P. K. (2019). An event-based sediment yield and runoff modeling using soil moisture balance/budgeting (SMB) method. Water Resources Management, 33, 3721-3741.https://doi.org/10.1007/s11269-019-02329-1.
Khan, M. F., Aadil, F., Maqsood, M., Khan, S., & Bukhari, B. H. (2018). An efficient optimization technique for node clustering in VANETs using gray wolf optimization. KSII Transactions on Internet and Information Systems (TIIS), 12(9), 4228-4247.‏
Kisi, O., Karahan, M., Sen, Z. (2006). River-suspended sediment modeling using the fuzzy logic approach. Hydrol Process, 20(2),4351-4362.10.1002/hyp.6166.
Mohammed, M.A.A., Khleel, N.A.A., Szabó, N.P., Szűcs , P. (2023). Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan. Model. Earth Syst. Environ. 9, 2501–2516.https://doi.org/10.1007/s40808-022-01638-6.
Nagy, H. M., Watanabe, K. A. N. D., & Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering, 128(6), 588-595.‏
Nourani, V., Ghaffari, A., Behfar, N., Foroumandi, E., Zeinali, A., Qing, C., Sankaran, A.(2024).Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools. Journal of Environmental Management.355(4),120-145.10.1016/j.jenvman.2024.120495 .
Nourani, V., Tajbakhsh, A.D., Molajou, A. (2018). Data mining based on wavelet and decision tree for rainfall-runoff simulation. Hydrol. Res, 50, 75–84. https://doi.org/ 10.2166/nh.2018.049.
Ostu, N. A. (1979). Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems Man and Cybernetics,9 (1), 62-66.10.1109/TSMC.1979.4310076.
Raj, C., & Singh, V. (2021). Spatial and temporal variation of fluvial islands and sandbars in River Ganga from Bhagalpur to Farakka during 1955– 2019. Sustainable Water Resources Management, 7(3), 22-38.https://doi.org/10.1007/s40899-021-00522-y.
Shin, S., Kyung, D., Lee, S., Taik & Kim, J., and Hyun, J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(4), 127-135.https://doi.org/10.1016/j.eswa.2004.08.009.
Vapnik, V., Chervonenkis, A.(1991). The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis,1(3), 283-305.10.12691/jgg-2-3-9.
Vapnik, V.N. (1995).The nature of statistical learning theory. Springer, New York, 3(1), 250-320.https://doi.org/10.1007/978-1-4757-3264-1.
Vapnik, V.N.(1998). Statistical learning theory. Wiley, New York, 4(1), 250-320. https://doi.org/10.1007/978-1-4757-3264-1.
Wang, D., Safavi, A.A., and Romagnoli, J.A.(2000). Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal,46(4),  1607-1615. https://doi.org/10.1002/aic.690460812.
Yan, X., Zhu, Y., Wu, J., Chen, H.(2012). An improved firefly algorithm with adaptive strategies. Adv Sci Lett, 16(1), 249–254.https://doi.org/10.1016/j.eswa.2020.113340.
Yang, X.S.(2008). Firefly algorithm, nature-inspired meta-heuristic algorithms. Wiley Online Libr, 20, 79–90.https://doi.org/10.1007/978-3-642-29694-9_16.
Zeidalinejad, N., Dehghani, R.(2023). Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways. Groundwater for Sustainable Development, 20(4), 112-132.https://doi.org/10.1016/j.gsd.2022.100882.
Zhu, Y. M., Lu, X. X., & Zhou, Y. (2007). Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology, 84(1-2), 111-125.‏
Journal of Aquifer and Qanat
Volume 5, Issue 1 - Serial Number 8
December 2024
Pages 125-138
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