Adhikary, S., & Gupta, A. (2011). MODELING GROUNDWATER FLOW AND SALINITY INTRUSION BY ADVECTIVE TRANSPORT IN THE REGIONAL UNCONFINED AQUIFER OF SOUTHWEST BANGLADESH.
Arora, S., & Keshari, A. K. (2021). ANFIS-ARIMA modelling for scheming re-aeration of hydrologically altered rivers. Journal of Hydrology, 601, 126635. https://doi.org/10.1016/J.JHYDROL.2021.126635.
Azimi, S., & Azhdary Moghaddam, M. (2020). Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index. Water Resources Management 2020 34:4, 34(4), 1369–1405. https://doi.org/10.1007/S11269-020-02507-6.
Banerjee, P., Singh, V. S., Chatttopadhyay, K., Chandra, P. C., & Singh, B. (2011). Artificial neural network model as a potential alternative for groundwater salinity forecasting. Journal of Hydrology, 398(3–4), 212–220. https://doi.org/10.1016/J.JHYDROL.2010.12.016
Çamdevýren, H., Demýr, N., Kanik, A., & Keskýn, S. (2005). Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling, 181(4), 581–589. https://doi.org/10.1016/J.ECOLMODEL.2004.06.043
Campbell, C. (2002). Kernel methods: A survey of current techniques. Neurocomputing, 48(1–4), 63–84. https://doi.org/10.1016/S0925-2312(01)00643-9
Che Nordin, N. F., Mohd, N. S., Koting, S., Ismail, Z., Sherif, M., & El-Shafie, A. (2021). Groundwater quality forecasting modelling using artificial intelligence: A review. Groundwater for Sustainable Development, 14, 100643. https://doi.org/10.1016/J.GSD.2021.100643
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., Liu, F., Zuo, M., Zou, X., Wang, J., Zhang, Y., Chen, D., Chen, X., Deng, Y., & Ren, H. (2020). Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171, 115454. https://doi.org/10.1016/J.WATRES.2019.115454
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. https://doi.org/10.1017/CBO9780511801389
Cuest Cordoba, G. A., Tuhovčák, L., & Tauš, M. (2014). Using Artificial Neural Network Models to Assess Water Quality in Water Distribution Networks. Procedia Engineering, 70, 399–408. https://doi.org/10.1016/J.PROENG.2014.02.045
El-Shafie, A., Taha, M. R., & Noureldin, A. (2006). A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resources Management 2006 21:3, 21(3), 533–556. https://doi.org/10.1007/S11269-006-9027-1
Elkiran, G., Nourani, V., & Abba, S. I. (2019). Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577, 123962. https://doi.org/10.1016/J.JHYDROL.2019.123962.
Guneshwor, L., Eldho, T. I., & Vinod Kumar, A. (2018). Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization. Water Resources Management 2018 32:4, 32(4), 1517–1538. https://doi.org/10.1007/S11269-017-1885-1
Huang, G. Bin, Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513–529. https://doi.org/10.1109/TSMCB.2011.2168604
Huang, G. Bin, Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE International Conference on Neural Networks - Conference Proceedings, 2, 985–990. https://doi.org/10.1109/IJCNN.2004.1380068
Jamei, M., Ahmadianfar, I., Chu, X., & Yaseen, Z. M. (2020). Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach. Journal of Hydrology, 589, 125335. https://doi.org/10.1016/J.JHYDROL.2020.125335
Kadkhodazadeh, M., & Farzin, S. (2021). A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters. https://doi.org/10.21203/RS.3.RS-465707/V1
Liang, N. Y., Huang, G. Bin, Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17(6), 1411–1423. https://doi.org/10.1109/TNN.2006.880583
Majumder, P., & Eldho, T. I. (2020). Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation. Water Resources Management 2020 34:2, 34(2), 763–783. https://doi.org/10.1007/S11269-019-02472-9
Mustapha, A., & Abdu, A. (2012). Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment. Journal of Environment and Earth Science, 2(2), 16–23. https://www.iiste.org/Journals/index.php/JEES/article/view/1516
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2020). Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machines. Environmental Earth Sciences 2020 79:19, 79(19), 1–25. https://doi.org/10.1007/S12665-020-09190-1
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2021). Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks. Water and Environment Journal, 35(1), 67–83. https://doi.org/10.1111/WEJ.12595
Rajaee, T., Khani, S., & Ravansalar, M. (2020). Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems, 200, 103978. https://doi.org/10.1016/J.CHEMOLAB.2020.103978
Sapankevych, N., & Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2), 24–38. https://doi.org/10.1109/MCI.2009.932254
Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Adaptive computation and machine learning. 626.
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least Squares Support Vector Machines. https://doi.org/10.1142/5089
Vaheddoost, B., & Aksoy, H. (2018). Interaction of groundwater with Lake Urmia in Iran. Hydrological Processes, 32(21), 3283–3295. https://doi.org/10.1002/hyp.13263
Valyon, J. Horvath, G. (2007). (PDF) Extended Least Squares LS-SVM. World Academy of Science, Engineering and Technology, 36. https://www.researchgate.net/publication/242532586_Extended_Least_Squares_LS-SVM
Wang, P., Yao, J., Wang, G., Hao, F., Shrestha, S., Xue, B., Xie, G., & Peng, Y. (2019). Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Science of The Total Environment, 693, 133440. https://doi.org/10.1016/J.SCITOTENV.2019.07.246
Zhang, Yanyang, Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. https://doi.org/10.1016/J.WATRES.2019.114888
Zhang, Yishan, Wu, L., Deng, L., & Ouyang, B. (2021). Retrieval of Water Quality Parameters from Hyperspectral Images Using a Hybrid Feedback Deep Factorization Machine Model. Water Research, 117618. https://doi.org/10.1016/J.WATRES.2021.117618
)