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.