Choubin, B.,
Malekian, A.,
Sajedi Hosseini, F., &
Rahmati, O. (2014). Water Table Prediction by Using Time Series Models and Adaptive Neural Fuzzy Inference System. Iranian Journal of Soil and Water Research,
45(1), 19-28. (In Farsi)
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.
Daliakopoulos, I. N., Coulibaly, P., & Tsanis, I. K. (2005). Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1-4), 229-240.
Fallah-Mehdipour, E., Haddad, O. B., & Mariño, M. (2013). Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research, 7(4), 253-260.
Feng, S., Kang, S., Huo, Z., Chen, S., Mao, X. (2008). Neural networks to simulate regional groundwater levels affected by human activities. Groundwater 46(1), 80–90.
Jalalkamali, A., Sedghi, H., & Manshouri, M. (2011). Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran. Journal of hydroinformatics, 13(4), 867-876.
Jeihouni, E., Eslamian, S., Mohammadi, M., & Zareian, M. J. (2019). Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran. Environmental Earth Sciences, 78(10), 293.
Khaki, M., Yusoff, I., & Islami, N. (2015). Simulation of groundwater level through artificial intelligence system. Environmental Earth Sciences, 73(12), 8357-8367.
Kholghi, M., & Hosseini, S. (2009). Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environmental Modeling & Assessment, 14(6), 729.
Kouziokas, G. N., Chatzigeorgiou, A., & Perakis, K. (2018). Multilayer feed forward models in groundwater level forecasting using meteorological data in public management. Water Resources Management, 32(15), 5041-5052.
Malekzadeh, M.,
Kardar, S.,
Shabanlou, S. (2020). Simulation of Groundwater Level Using the Hybrid Model Wavelet-Self Adaptive Extreme Learning Machine. Iranian Journal of Soil and Water Research. 51(4), 975-986. (In Farsi)
Ministry of Energy, (2018). Report on the extension and development of the aquifer ban in Golpayegan study area (code 4130), Iran Water Resources Management Company, Isfahan Regional Water Company, Integration and balance group, 202 pages. (In Farsi)
Mirzavand, M., Khoshnevisan, B., Shamshirband, S., Kisi, O., Ahmad, R., & Akib, S. (2015). Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study. Natural Hazards, 1(1), 1-15.
Mohammadi Ghaleni, M., Ebrahimi, K., and Araghinejad, Sh. (2013). Evaluation impact of drought, extraction and construction of dam on the groundwater drop-case study Saveh aquifer. Journal of Water and Soil Conservation, 19(4), 189-200. (In Farsi)
Moravej, M., Amani, P., & Hosseini-Moghari, S.-M. (2020). Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR). Groundwater for Sustainable Development, 11, 100447.
Mukherjee, A., & Ramachandran, P. (2018). Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India: Analysis of comparative performances of SVR, ANN and LRM. Journal of Hydrology, 558, 647-658.
Nie, S., Bian, J., Wan, H., Sun, X., & Zhang, B. (2017). Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models. Journal of Water Supply: Research and Technology—AQUA, 66(1), 15-24.
Nourani, V., Mogaddam, A. A., & Nadiri, A. O. (2008). An ANN‐based model for spatiotemporal groundwater level forecasting. Hydrological Processes: An International Journal, 22(26), 5054-5066.
Rajaee, T., Ebrahimi, H., & Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology, 572, 336-351.
Rakhshandehroo, G. R., Vaghefi, M., & Aghbolaghi, M. A. (2012). Forecasting groundwater level in Shiraz plain using artificial neural networks. Arabian Journal for Science and Engineering, 37(7), 1871-1883.
Razaghdoust, E.,
Mohammadnezhad, B.,
Kardan Moghaddam, H. (2020). Spatio-temporal Analysis of Groundwater Level Using Clustering Method Combined with Artificial Neural Network, Iranian Journal of Soil and Water Research,
51(4), 801-812. (In Farsi)
Sadat-Noori, M., Glamore, W., & Khojasteh, D. (2020). Groundwater level prediction using genetic programming: the importance of precipitation data and weather station location on model accuracy. Environmental Earth Sciences, 79(1), 37.
Seifi, A., Ehteram, M., Singh, V. P., & Mosavi, A. (2020). Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN. Sustainability, 12(10), 4023.
Shiri, J., Kisi, O., Yoon, H., Lee, K.-K., & Nazemi, A. H. (2013). Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques. Computers & Geosciences, 56, 32-44.
Shirmohammadi, B., Vafakhah, M., Moosavi, V., & Moghaddamnia, A. (2013). Application of several data-driven techniques for predicting groundwater level. Water Resources Management, 27(2), 419-432.
Suryanarayana, C., Sudheer, C., Mahammood, V., Panigrahi, B.K., (2014). An integrated wavelet- support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145, 324–335.
Tang, Y., Zang, C., Wei, Y., & Jiang, M. (2019). Data-driven modeling of groundwater level with Least-Square support vector machine and spatial–temporal analysis. Geotechnical and Geological Engineering, 37(3), 1661-1670.
Yoon, H., Hyun, Y., Ha, K., Lee, K.-K., & Kim, G.-B. (2016). A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions. Computers & Geosciences, 90, 144-155.