شبیه‌سازی نوسانات سطح آب زیرزمینی با استفاده از مدل‌های یادگیری ماشین رگرسیون خطی (SVR) و یادگیری افراطی (ELM)

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Ahmed, H. U., Mostafa, R. R., Mohammed, A., Sihag, P., & Qadir, A. (2023). Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete. Neural Computing and Applications, 35(3), 2909-2926. https://doi.org/10.1007/s00521-022-07724-1

Alizamir, M., Kazemi, Z., Kazemi, Z., Kermani, M., Kim, S., Heddam, S., Kisi, O., & Chung, I.-M. (2023). Investigating landfill leachate and groundwater quality prediction using a robust integrated artificial intelligence model: Grey wolf metaheuristic optimization algorithm and extreme learning machine. Water, 15(13), 2453. https://doi.org/10.3390/w15132453

Aman Jalili, A., Najarchi, M., Shabanlou, S., & Jafarinia, R. (2023). Multiobjective optimization of water resources in real time based on integration of NSGA-II and support vector machines. Environ Sci Pollut Res, 30,16464–16475. https://doi.org/10.1007/s11356-022-22723-4

Amiri, S., Rajabi, A., Shabanlou, S. Yosefvand, F., & Izadbakhsh, M.A. (2023). Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Sci Inform, 16, 3227–3241. https://doi.org/10.1007/s12145-023-01052-1

Azizi, E., Yosefvand, F., Yaghoubi, B., Izadbakhsh, M.A., & Shabanlou, S. (2023) Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran. Irrigation and Drainage, 72(3), 747–762. https://doi.org/10.1002/ird.2794

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Esmaeili, F., Shabanlou, S., & Saadat, M. (2021). A wavelet-outlier robust extreme learning machine for rainfall forecasting in Ardabil City, Iran. Earth Sci Inform. 14, 2087–2100. https://doi.org/10.1007/s12145-021-00681-8

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Li, J., Lu, W., Wang, H., Fan, Y., & Chang, Z. (2020). Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine. Journal of Hydrology, 584, 124657. https://doi.org/10.1016/j.jhydrol.2020.124657

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Mazraeh, A., Bagherifar, M., Shabanlou, S., & Ekhlasmand, R. (2023). A hybrid machine learning model for modeling nitrate concentration in water sources. Water, Air, & Soil Pollution, 234(11), 721. http://dx.doi.org/10.1007/s11270-023-06745-3

Mazraeh, A., Bagherifar, M., Shabanlou, S., & Ekhlasmand, R. (2024). A novel committee-based framework for modeling groundwater level fluctuations: A combination of mathematical and machine learning models using the weighted multi-model ensemble mean algorithm. Groundwater for Sustainable Development, 24, 101062. https://doi.org/10.1016/j.gsd.2023.101062

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Moghadam, R.G., Yaghoubi, B., Rajabi, A., Shabanlou, S., & Izadbakhsh, M.A. (2022). Evaluation of discharge coefficient of triangular side orifices by using regularized extreme learning machine. Appl Water Sci, 12, 145. https://doi.org/10.1007/s13201-022-01665-9

Mohammed, K.S., Shabanlou, S., Rajabi, A., Yosefvand, F., & Izadbakhsh, M.A. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Appl Water Sci, 13, 54 (2023). https://doi.org/10.1007/s13201-022-01861-7

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Panahi, J., Mastouri, R., & Shabanlou, S. (2022). Insights into enhanced machine learning techniques for surface water quantity and quality prediction based on data pre-processing algorithms. J Hydroinf, 24(4), 875–897. https://doi.org/10.2166/hydro.2022.022

Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, 588, 125033. https://doi.org/10.1016/j.jhydrol.2020.125033

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