Evaluation of Hybrid Metaheuristic Models in Estimating Electrical Conductivity (Case Study: Kakarza River, Lorestan Province)

نوحانی, ابراهیم; باباعلی, حمیدرضا; دهقانی, رضا

doi:10.22077/jdcr.2025.9196.1135

Evaluation of Hybrid Metaheuristic Models in Estimating Electrical Conductivity (Case Study: Kakarza River, Lorestan Province)

نوع مقاله : مقاله پژوهشی

نویسندگان

ابراهیم نوحانی ¹

حمیدرضا باباعلی ²

رضا دهقانی ³

¹ استادیارگروه عمران، مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

² دانشیار، گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد خرم آباد، خرم آباد، ایران

³ بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان لرستان، خرم آباد، ایران.

https://doi.org/10.22077/jdcr.2025.9196.1135

چکیده

Electrical conductivity (EC) is an important indicator for monitoring water quality in rivers. Electrical conductivity is inherently related to the concentration of dissolved ionic compounds present in aquatic environments, including various salts and minerals. Estimating electrical conductivity is crucial for environmental monitoring and assessing the overall health of aquatic ecosystems. In this research, a hybrid intelligent model based on the Support Vector Regression (SVR) approach was developed to estimate the electrical conductivity of river water. For this purpose, three optimization algorithms, including Wavelet, Whale, and Particle Swarm Optimization (PSO), were used to model the electrical conductivity of the river flow. For modeling, statistics and information from the Kakarza River hydrometric station, located in Lorestan province, were used as a case study in 7 combined scenarios of input parameters for the years 2003-2023. To evaluate the performance of the models, the evaluation criteria of correlation coefficient, root mean square error, mean absolute error, and Nash-Sutcliffe coefficient were used. The results showed that combined scenarios in the models under investigation improve the model performance. Furthermore, the results obtained from the evaluation criteria showed that the Support Vector Regression-Wavelet model has a correlation coefficient of 0.980, a root mean square error of 0.344 (ppm), a mean absolute error of 0.172 (ppm), and a Nash-Sutcliffe coefficient of 0.985 in the validation phase. Overall, the results showed that the use of intelligent models based on the support vector regression approach can be an effective approach in river engineering sustainability.

کلیدواژه‌ها

Kakarza River

Water Quality

Electrical Conductivity

Wavelet

موضوعات

خشکسالی

Basak, D., Pal, S., and Patranabis, D.C. (2007). Support vector regression. Neural Information Process, 11(2), 203-225.https://doi.org/10.1007/978-1-4302-5990-9_4

Bhat, S.U., Nisa, A.U., Sabha, I., Mondal, N.C. (2022). Spring water quality assessment of Anantnag district of Kashmir Himalaya: towards understanding the looming threats to spring ecosystem services. Applied Water Science, 12,180-198. https://doi.org/10.1007/s13201-022-01691-7

Dehghani, R., Babaali, H. Zeidalinejad, N. (2023). Evaluation of Statistical Models and Modern Hybrid Artificial Intelligence in Simulation of Runoff Precipitation Process. Sustainable Water Resources Management, 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

Eberhart, R., Kennedy, J. (1995). A New Optimizer Using Particle Swarm Theory Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Piscataway, NJ: IEEE Service Center 5, 39-43. 10.1109/MHS.1995.494215

Ekemen Keskin, T., Özler, E., Şander, E. (2020). Prediction of electrical conductivity using ANN and MLR: a case study from Turkey. Acta Geophysica, 68, 811–820. https://doi.org/10.1007/s11600-020-00424-1

Ezea, V.C., Ihedioha, J.N., Abugu, H.O., Ekere, N.R. (2022). A multi-criteria approach to drinking and irrigation water assessment of spring water in Igbo-Etiti. Nigeria Applied Water Science, 12, 220-241.https://doi.org/10.1007/s13201-022-01747-8

Giri, A., Bharti, V.K., Kalia, S., Kumar, K., Khansu, M. (2022). Hydrochemical and quality assessment of irrigation water at the trans-himalayan high-altitude regions of Leh, Ladakh. India. Applied Water Science, 12,197-211. https://doi.org/10.1007/s13201-022-01716-1

Jalalkamali, A., JalalKamali, N. (2018). Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis. Journal of Artificial Intelligence & Data Mining, 6(2), 439-445. https://doi.org/10.22044/jadm.2017.1086

Khadr, M., Elshemy, M. (2017). Data-driven modeling for water quality prediction case study: the drains system associated with Manzala Lake. Egypt Ain Shams Engineering Journal 8, 549–557. https://doi.org/10.1016/j.asej.2016.08.004

Kisi, O., Karahan, M., Sen, Z. (2006). River suspended sediment modeling using fuzzy logic approach. Hydrological Process, 20(2), 4351-4362. https://doi.org/10.1002/hyp.6166

Kumar, D., Singh, V.K., Abed, S.A., Tripathi, V.K., Gupta, S., Al-Ansari, N., Vishwakarma, D.K., Dewidar, A., Al‑Othman, A., Mattar, M. (2023). Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms. Applied Water Science, 13, 192-214. https://doi.org/10.1007/s13201-023-02005-1

Lakrout, A., Meddah, B., Ali Rahmani, S.E. (2022). Geochemical assessment of thermal water in Bouhanifia aquifer system: suitability for touristic therapeutic treatment. Applied Water Science, 12, 204-221. https://doi.org/10.1007/s13201-022-01727-y

Liu, Z., Feng, S., Zhang, D. (2023). Effects of precipitation, irrigation, and exploitation on groundwater geochemical evolution in the people’s victory canal irrigation area. China Applied Water Science,13,1-18. https://doi.org/10.1007/s13201-022-01808-y

Mensah-Akutteh, H., Buamah, R., Wiafe, S., Nyarko, K.B. (2022). Optimizing coagulation–flocculation processes with aluminium coagulation using response surface methods. Applied Water Science, 12,188-200. https://doi.org/10.1007/s13201-022-01708-1

Mirjalili, S., and Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95(6):51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008

Mokhtar, A., Elbeltagi, A., Gyasi-Agyei, Y. (2022). Prediction of irrigation water quality indices based on machine learning and regression models. Applied Water Science, 12,76-88. https://doi.org/10.1007/s13201-022-01590-x

Nagy, , Watanabe, K., Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulics Engineering 128, 558-559. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:6(588)

Naiel, M,A,E,, Abdelghany, M.F., Khames, D.K. (2022). Administration of some probiotic strains in the rearing water enhances the water quality, performance, body chemical analysis, antioxidant and immune responses of Nile tilapia. Oreochromis Niloticus. Applied Water Science, 12, 209-222. https://doi.org/10.1007/s13201-022-01733-0

Najah Ahmed, A., Binti Othman, F., Abdulmohsin Afan, H. (2019). Machine learning methods for better water quality prediction. Journal of Hydrology. 578,124-138. https://doi.org/10.1016/j.jhydrol.2019.124084

Nemčić-Jurec, J., Ruk, D., Oreščanin, V., Kovač, I., Ujević Bošnjak, M., Stephen Kinsela , A. (2022). Groundwater contamination in public water supply wells: risk assessment, evaluation of trends and impact of rainfall on groundwater quality. Applied Water Science, 12, 172-195. https://doi.org/10.1007/s13201-022-01697-1

Rajaee, T., Khani, S., Ravansalar, M. (2022). Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems, 200(3), 1039-1055. https://doi.org/10.1016/j.chemolab.2020.103978

Reddy, , Saha, A.K. (2022). A modified Whale Optimization Algorithm for exploitation capability and stability enhancement. Heliyon, 8(10),425-441. https://doi.org/10.1016/j.heliyon.2022.e11027.

Shams, M.Y., Elshewey, A.M., El-kenawy, ES.M., Ibrahim, A., Talaat, F.,Tarek, Z. (2025). Water quality prediction using machine learning models based on grid search method. Multimedia Tools and Applications, 83, 35307–35334. https://doi.org/10.1007/s11042-023-16737-4

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

Shrivatava, M., Prasad, V., Khare, R.(2015). Multi-objective optimization of water distribution system using particle swarm optimization. IOSR Journal of Mechanical and Civil Engineering,12(1), 21–28. DOI: 10.9790/1684-12612128

Vapnik, , Chervonenkis, A.(1991). The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis, 1(3), 283-305.10.1007/s10444-004-7634-z

Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York. https://doi.org/10.1007/978-1-4757-3264-1

Vapnik, V.N. (1998). Statistical learning theory. Wiley, New York. https://doi.org/10.1109/72.788640

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

Wang, K.,Liu, L.,Ben, X.,Jin, D.,Zhu, Y.,Wang, F. (2024). Hybrid deep learning based prediction for water quality of plain watershed. Environmental Research, 262(3),119-132. https://doi.org/10.1016/j.envres.2024.119911

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.