[1]. Kisi, O., Akbari, N., Sanatipour, M., Hashemi, A., Teimourzadeh, K. & Shiri, J. 2013. Modeling of dissolved oxygen in river water using artificial intelligence techniques. Journal of Environmental Informatics, 22(2), 92-101. https://doi.org/10.3808/jei.201300248
[2]. Ravansalar, M. & Rajaee, T. 2015. Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model. Environmental Monitoring and Assessment, 187(6), 366. https://doi.org/10.1007/s10661-015-4600-9
[3]. Kisi, O. & Parmar, K. S. 2016. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104-112. https://doi.org/10.1016/j.jhydrol.2015.12.014
[4]. Rajaee, T. & Shahabi, A. 2016. Evaluation of wavelet-GEP and wavelet-ANN hybrid models for prediction of total nitrogen concentration in coastal marine waters. Arabian Journal of Geosciences, 9(3), 176. https://doi.org/10.1007/s12517-015-2236-0
[5]. Bozorg-Haddad, O., Soleimani, S. & Loáiciga, H. A. 2017. Modeling water-quality parameters using genetic algorithm-least squares support vector regression and genetic programming. Journal of Environmental Engineering, 143(7), 04017021. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001216
[6]. Azad, A., Karami, H., Farzin, S., Mousavi, S. F. & Kisi, O. 2019. Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Science and Engineering, 12(1), 45-54. https://doi.org/10.1016/j.wse.2018.11.001
[7]. Aghel, B., Rezaei, A. & Mohadesi, M. 2019. Modeling and prediction of water quality parameters using a hybrid particle swarm optimization-neural fuzzy approach. International Journal of Environmental Science and Technology, 16(8), 4823-4832. https://doi.org/10.1007/s13762-019-02331-6
[8]. Haghiabi, A. H., Nasrolahi, A. H. & Parsaie, A. 2018. Water quality prediction using machine learning methods. Water Quality Research Journal, 53(1), 3-13. https://doi.org/10.2166/wqrj.2018.025
[9]. Wu, J. and Wang, Z., 2022. A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water, 14(4), p.610. https://doi.org/10.3390/w14040610
[10]. Tian, X., Wang, Z., Taalab, E., Zhang, B., Li, X., Wang, J., Ong, M.C. and Zhu, Z., 2022. Water quality predictions based on grey relation analysis enhanced LSTM algorithms. Water, 14(23), p.3851. https://doi.org/10.3390/w14233851
[11]. Gao, Z., Chen, J., Wang, G., Ren, S., Fang, L., Yinglan, A. and Wang, Q., 2023. A novel multivariate time series prediction of crucial water quality parameters with Long Short-Term Memory (LSTM) networks. Journal of Contaminant Hydrology, 259, p.104262. https://doi.org/10.1016/j.jconhyd.2023.104262
[12]. Cai, H., Zhang, C., Xu, J., Wang, F., Xiao, L., Huang, S. and Zhang, Y., 2023. Water quality prediction based on the KF-LSTM encoder-decoder network: A case study with missing data collection. Water, 15(14), p.2542. https://doi.org/10.3390/w15142542
[13]. Ruan, J., Cui, Y., Song, Y. and Mao, Y., 2023. A novel RF-CEEMD-LSTM model for predicting water pollution. Scientific Reports, 13(1), p.20901. https://doi.org/10.1038/s41598-023-48409-6
[14]. Chen, S., Huang, J., Wang, P., Tang, X. and Zhang, Z., 2024. A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation. Water Research, 248, p.120895. https://doi.org/10.1016/j.watres.2023.120895
[15]. Bi, J., Zhang, L., Yuan, H. and Zhang, J., 2023. Multi-indicator water quality prediction with attention-assisted bidirectional LSTM and encoder-decoder. Information Sciences, 625, pp.65-80. https://doi.org/10.1016/j.ins.2022.12.091
[16]. Jang, J. S. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541