اثرات اجرای اقدامات سازه ای و تغذیه مصنوعی بر نوسانات تراز آب زیرزمینی دشت فامنین

Amiri, S., Rajabi, A., Shabanlou, S., Yosefvand, F. & izadbakhsh, MA. (2023). Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Science Informatic. https://doi.org/10.1007/s12145-023-01052-1

Azizi, E., Yosefvand, F., Yaghoubi, B., Izadbakhsh, MA. & 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.

Azizi, K., Azari, A., & Farhadi Bansouleh, B. (2023). Simulation and determination of hydrodynamic coefficients and aquifer balance with Modflow mathematical model (Case study: Kermanshah Plain). Advanced Technologies in Water Efficiency, 2(4), 68-87. [in Persian]

Azizpour, A., Izadbakhsh, MA., Shabanlou, S., Yosefvand, F. & Rajabi, A. (2021). Estimation of water level fluctuations in groundwater through a hybrid learning machine, Groundwater for Sustainable Development, 15, 100687.

Azizpour, A., Izadbakhsh, MA., Shabanlou, S., Yosefvand, F. & Rajabi, A. (2022). Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model. Environment Science Pollution Research, 29, 28414–28430.

Bayesteh, M. & Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning and Management, 147(2), 04020099.

Esmaeili, F., Shabanlou, S. & Saadat, MA. (2021). Wavelet-outlier robust extreme learning machine for rainfall forecasting in Ardabil City. Iran. Earth Sci Inform. 14, 2087–2100.

Fallahi, MM., Shabanlou, S., Rajabi, A., Yosefvand, F. & izadbakhsh, MA. (2023). Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Applied Water Science. 13, 143.

Ghobadian, R., Fatahi Ghaghabagi, A. & Zare, M. (2015). The effect of construction of irrigation and drainage network of Gavshan dam on underground water resources of Miandarband plain using GMS 6.5 model. Water research in agriculture (soil and water sciences), 28 (4), 759-772.

Guzman, SM., Paz, JO., Tagert, M. L. M. & Mercer, A. E. (2019). Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environmental Modeling & Assessment, 24(2), 223-234.

Hu, L., Xu, Z. & Huang, W. (2016). Development of a river-groundwater interaction model and its application to a catchment in Northwestern China. Hydrology, 543, 483–500.

Ivkovic, K. M. (2009). A top–down approach to characterise aquifer–river interaction processes. Hydrology, 365, 145–155.

Kamkar, V., Azari, A., & Fatemi, S. E. (2021). Estimation of Recharge and Flow Exchange between River and Aquifer Based on Coupled Surface Water-Groundwater Model. Iranian Journal of Soil and Water Research, 52(7), 1779-1793. [in Persian]

Lu, C., Chen, Y., Zhang, C., & Luo, J. (2013). Steady-state freshwater–seawater mixing zone in stratified coastal aquifers. Hydrology, 505, 24-34.

Mahdavi, M., Farokhzadeh, B., Salajegheh. A., Malakian, A & Soori, M. (2012). Simulation of Hamadan-Bahar plain aquifer and management scenarios analysis using PMWIN model. Watershed research (research and construction), 26 (1), 108-116.

mazandarani zadeh, H., & hoseini, M. (2023). Investigating the effect of agricultural product price forecasting on groundwater level using systems dynamics, in order to simultaneously maintain the welfare of farmers and groundwater resources. Iranian Journal of Soil and Water Research, 53(11), 2565-2582. [in Persian]

Mohtsham, M., Dehghani, A.A., Akbarpour, A., & Miftah Halaghi, M. (2011). Prediction of water level in aquifer using GMS software, case study: Birjand aquifer, 4th Iran Water Resources Management Conference, Tehran, Iran. [in Persian]

Nagheli, S., Samani, N., & Pasandi, M. (2011). Investigation of balance and sustainable development of Najaf Abad aquifer, 30th Earth Sciences Meeting, Tehran, Iran. [in Persian]

Malekzadeh, M., Kardar, S., Saeb, K., Shabanlou, S. & Taghavi L. (2019a). A novel approach for prediction of monthly ground water level using a hybrid wavelet and non-tuned self-adaptive machine learning model. Water resources management. 33, 1609-1628.        

Malekzadeh, M., Kardar, S. & Shabanlou, S. (2019b). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models. Groundwater for Sustainable Development. 9, 100279.

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), 1-22.

Mohammed, KS., Shabanlou, S., Rajabi, A., Yosefvand, F. & izadbakhsh, MA. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Applied Water Science. 13, 54.

Moradi, A., Akhtari, A. & Azari, A. (2023). Prediction of groundwater level fluctuation using methods based on machine learning and numerical model. Applied Research in Water and Wastewater, 10 (1), 20-28.

Nadiri, A. A., Naderi, K., Khatibi, R., & Gharekhani, M. (2019). Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological sciences journal, 64(2), 210-226.

Narula, K.K. & Gosian, A.K. (2013). Modeling hydrology, groundwater recharge and non-point nitrate loadings in the Himalayan Upper Yamuna basin. Science of the Total Environment. S102-S116.

Pahar, G. & Dhar, A. (2014). A Dry Zone-Wet Zone Based Modeling of Surface Water and Groundwater Interaction for Generalized Ground Profile. Hydrology, 519(27), 2215-2223.

Pourhaghi, A., Akhondali. A. A., Radmanesh, F. & Mirzaee, S. Y. (2015). Manage the Groundwater Sources Exploration of the Nourabad Plain in the Drought Conditions with MODFLOW Modeling. Irrigation science and engineering, 37 (92), 71-82.

Poursaeid, M., Mastouri, R., Shabanlou, S. & Najarchi, M. (2020). Estimation of total dissolved solids, electrical conductivity, Salinity and groundwater levels using novel learning machines. Environment Earth Science. 79, 1–25.

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, 67–83.

Sgarifi, F. & ghafouri, A.R. (1997). Flood water spreading in Iran and Integrated Aproach. rain drope, series 2, vol 7: i-iii-1997

Shamsai, A., & Forghani, A. (2011). Conjunctive use of Surface and Ground Water Resources in Arid Regions. Iran-Water Resources Research, 7(2), 26-36. [in Persian]

Soltani, K., & Azari, A. (2022). Forecasting groundwater anomaly in the future using satellite information and machine learning. Hydrology, 612 (2), 128052.

Yosefvand, F. & Shabanlou, S. (2020). Forecasting of Groundwater Level Using Ensemble Hybrid Wavelet–Self-adaptive Extreme Learning Machine-Based Models. Natural Resource Research. 29, 3215–3232.

Zeinali, M., Azari, A. & Heidari, M. (2020a). Simulating Unsaturated Zone of Soil for Estimating the Recharge Rate and Flow Exchange Between a River and an Aquifer. Water Resources Management, 34, 425–443.

Zeinali, M., Azari, A. & Heidari, M. (2020b). Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. Water Resource Planning and Management (ASCE), 146(5), 04020020.

Zibaei, M. H., Zibaei, M. & Ardokhani, K. (2013). Evaluation of scenarios of integrated use of surface and groundwater resources in Firoozabad plain of Fars. Agricultural Economics Research, 5(1), 157-181.