شبیه‌سازی سطح آب زیرزمینی آبخوان دشت اراک با استفاده از مدل MODFLOW و شبکه عصبی مصنوعی مبتنی بر روش دسته‌بندی گروهی داده‌ها (GMDH)

Ahmadi, S. H., & Sedghamiz, A. (2007). Geostatistical analysis of spatial and temporal variations of groundwater level. Environmental monitoring and assessment, 129(1), 277-294. https://doi.org/10.1007/s10661-006-9361-z

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

Arabgol, R., Sartaj, M., & Asghari, K. (2016). Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environmental Modeling & Assessment, 21(1), 71-82. https://doi.org/10.1007/s10666-015-9468-0

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

Bear, J. (2012). Hydraulics of groundwater. Courier Corporation, Mineola, New York. https://www.scirp.org/reference/referencespapers?referenceid=2874426

Bloomfield, J., Marchant, B., Bricker, S., & Morgan, R. (2015). Regional analysis of groundwater droughts using hydrograph classification. Hydrology and Earth System Sciences, 19(10), 4327-4344. https://doi.org/10.5194/hess-19-4327-2015

Chen, C., He, W., Zhou, H., Xue, Y., & Zhu, M. (2020). A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China. Scientific reports, 10(1), 1-13. https://doi.org/10.1038/s41598-020-60698-9

Choubin, B., & Malekian, A. (2017). Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environmental Earth Sciences, 76(15), 1-10. https://doi.org/10.1007/s12665-017-6870-8

Coulibaly, P., Anctil, F., Aravena, R., & Bobée, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4), 885-896. https://doi.org/10.1029/2000WR900368

Ebtehaj, I., Bonakdari, H., & Gharabaghi, B. (2018). Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement, 116, 473-482. https://doi.org/10.1016/j.measurement.2017.11.023

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(4), 1-14. https://doi.org/10.1007/s12145-021-00681-8

Fallahi, M.M., Shabanlou, S., Rajabi, A., Yosefvand, F., & IzadBakhsh, M.A. (2023). Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Appl Water Sci, 13(143). https://doi.org/10.1007/s13201-023-01949-8

Farlow, S. J. (1981). The GMDH algorithm of Ivakhnenko. The American Statistician, 35(4), 210-215. https://doi.org/10.1080/00031305.1981.10479358

Ghazanfari, N., Gholami, S., Emad, A., & Shekarchi, M. (2017). Evaluation of GMDH and MLP networks for prediction of compressive strength and workability of concrete. Bulletin de la Société Royale des Sciences de Liège, 86, 855-868. https://doi.org/10.25518/0037-9565.7032

Hálek, V., & Švec, J. (2011). Groundwater hydraulics. Elsevier Science imprintation, Amsterdam, Netherlands. https://shop.elsevier.com/books/groundwater-hydraulics/halek/978-0-444-99820-0

Harbaugh, A. W., Banta, E. R., Hill, M. C., & McDonald, M. G. (2000). Modflow-2000, the u. s. geological survey modular ground-water model-user guide to modularization concepts and the ground-water flow process. Open-file report. U. S. Geological Survey publications, Washington, United States of America. https://doi.org/10.3133/ofr200092

Ivakhnenko, A., & Ivakhnenko, G. (1995). The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern recognition and image analysis c/c of raspoznavaniye obrazov i analiz izobrazhenii, 5, 527-535.                                 https://articles.gmdh.net/review/algorith.pdf

Jakeman, A. J., Barreteau, O., Hunt, R. J., Rinaudo, J.-D., & Ross, A. (2016). Integrated groundwater management. Springer Open publications, New York City, United States of America. https://doi.org/10.1007/978-3-319-23576-9

Jalali, M., Karami, S., & Marj, A. F. (2016). Geostatistical evaluation of spatial variation related to groundwater quality database: case study for Arak plain aquifer, Iran. Environmental Modeling & Assessment, 21(6), 707-719. https://rd.springer.com/article/10.1007/s10666-016-9506-6

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. https://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

McLachlan, P., Chambers, J. E., Uhlemann, S. S., & Binley, A. (2017). Geophysical characterisation of the groundwater–surface water interface. Advances in Water Resources, 109, 302-319. https://doi.org/10.1016/j.advwatres.2017.09.016

Mirzavand, M., & Ghazavi, R. (2015). A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water resources management, 29(4), 1315-1328. https://doi.org/10.1007/s11269-014-0875-9

Moon, S.-K., Woo, N. C., & Lee, K. S. (2004). Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge. Journal of hydrology, 292(1-4), 198-209. https://doi.org/10.1016/j.jhydrol.2003.12.030

Neuman, S. P. (1972). Theory of flow in unconfined aquifers considering delayed response of the water table. Water Resources Research, 8(4), 1031-1045.                                          https://doi.org/10.1029/WR008i004p01031

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. Journal of Hydroinformatics, 24(4), 875–897. https://doi.org/10.2166/hydro.2022.022

Prickett, T. A., & Lonnquist, C. G. (1971). Selected digital computer techniques for groundwater resource evaluation. Bulletin (Illinois State Water Survey) no. 55, Urbana-Champaign, Chicago. https://hdl.handle.net/2142/94585

Sahoo, S., Russo, T., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878-3895. https://doi.org/10.1002/2016WR019933

Santos, S., Gaspar, A. T. F., & Schiozer, D. J. (2018). Comparison of risk analysis methodologies in a geostatistical context: Monte Carlo with joint proxy models and discretized latin hypercube. International Journal for Uncertainty Quantification, 8(1). https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018019782

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003

Seifi, A., Ehteram, M., Singh, V. P., & Mosavi, A. (2020). Modeling and uncertaintyanalysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN. Sustainability, 12(10), 4023. https://doi.org/10.3390/su12104023

Shabanlou, S. (2018). Improvement of extreme learning machine using self-adaptive evolutionary algorithm for estimating discharge capacity of sharp-crested weirs located on the end of circular channels. Flow Measurement and Instrumentation, 59, 63-71. https://doi.org/10.1016/j.flowmeasinst.2017.11.003

Todd, D. K., & Mays, L. W. (2004). Groundwater hydrology. John Wiley & Sons publications, New York City, United States of America. https://www.wiley.com/en-us/Groundwater+Hydrology%2C+3rd+Edition-p-9780471059370

Wang, H. F., & Anderson, M. P. (1995). Introduction to groundwater modeling: finite difference and finite element methods. Academic Press, Cambridge, Massachusetts, United States of America.                                                https://books.google.com/books/about/Introduction_to_Groundwater_Modeling.html?id=uJT-jwTZQW8C

Zhang, A., Winterle, J., & Yang, C. (2020). Performance comparison of physical process-based and data-driven models: a case study on the Edwards Aquifer, USA. Hydrogeology Journal, 28(6), 2025-2037. https://doi.org/10.1007/s10040-020-02169-z