- Banihabib, M. E., & Jamali, F. S. (2011). Comparison of Dynamic Artificial Neural Network and Multivariate Linear Regression Models for Inflow Forecasting Using Remote Sensing Data, 20(2), 173. (In Persian)
- Barnston, A. G., & Livezey, R. E. (1987). Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns. Monthly weather review, 115(6), 1083-1126.
- Hurrell, J. W. (1995). Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science, 269(5224), 676-679.
- Jones, P. D., Jónsson, T., & Wheeler, D. (1997). Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and southwest Iceland. International Journal of Climatology: A Journal of the Royal Meteorological Society, 17(13), 1433-1450.
- Saji, N. H., & Yamagata, T. J. C. R. (2003). Possible impacts of Indian Ocean dipole mode events on global climate. Climate Research, 25(2), 151-169.
- Mohammadi, K., Eslami, H. R., & Dayyani, D. S. (2005). Comparison of regression, ARIMA, and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). Journal of Agriculture-Science-Technology, 7, 17-30. (In Persian)
- Sohrabi, S., & Bozorg Haddad, O. (2007). The artificial neural network model in predicting the inflow to the reservoirs of dams, the fourth national conference of watershed science and engineering of Iran, watershed management, Karaj. (In Persian)
- Rubaai, A., Castro-Sitiriche, M. J., & Ofoli, A. R. (2008). Design and implementation of parallel fuzzy PID controller for high-performance brushless motor drives: an integrated environment for rapid control prototyping. IEEE Transactions on Industry Applications, 44(4), 1090-1098.
- Yarahmadi, D., & Azizi, Gh. (2008). Multivariate analysis of relationship between sesonal rainfall in iran with climate indices. Geographical Research Quarterly, 39(62), 161-174. (In Persian)
- Talebi, Z. (2012). The effect of Teleconnection pattern of the North Atlantic Oscillation (NAO) on reference evapotranspiration in the western regions of the Iran. Thesis. (In Persian)
- Farajzadeh, M., Ahmadi, M., Alijani, B., Qavidel Rahimi, Y., Mofidi, A., & Babaeian, I. (2013). Study on Variation of Major Teleconnection Patterns (MTP) associated with Iran’s Precipitation. Journal of Climate Research, 1392(15), 31-45. (In Persian)
- Beltram, L., & Carbonin, D. (2013). ENSO teleconnection patterns on large-scale water resources systems. Thesis.
- Kumar, S., Tiwari, M. K., Chatterjee, C., & Mishra, A. (2015). Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis, and bootstrap method. Water resources management, 29(13), 4863-4883.
- Azizi, G., Chehreara, T., & Safarrad, T. (2014). Simultaneous Effects of NAO and SOI Phases on Iran’s Climate. Geography and Environmental Sustainability, 4(3), 43-56. (In Persian)
- Salehizade, A. A., Rahmanian, M., Farajzadeh, M., & Ayoubi, A. (2015). Analysis of temperature changes on electricity consumption in Fars Province. Mediterranean Journal of Social Sciences, 6(3 S2), 610.
- Block, P. (2016). Tailoring seasonal climate forecasts for hydropower operations. Meteorology and Energy Security: Simulations, Projections, and Management, 179.
- Babaee Fini, O., & Fattahi, E. (2015). Seasonal Prediction of Discharge Entering into Uremia Lake by Using Climatic Large Scale Signals. Geography and Development, 13(40), 109-124. (In Persian)
- Ahmadi, F., Radmanesh, F., & Mir Abbasi Najaf Abadi, R. (2016). Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River). Irrigation Sciences and Engineering, 39(4), 213-223. (In Persian)
- Sedighi, F., Vafakhah, M., & Javadi, M. R. (2016). Application of Artificial Neural Network for Snowmelt-Runoff (Case Study: Latyan Dam Watershed). Journal of Watershed management research. 6 (12):43-54. (In Persian)
- Khosravi, D., & Mesgari, E. (2016). Spatial Analysis of Relationship Between Teleconnection Patterns and Monthly Temperature of Northwest Iran. Geography and Territorial Spatial Arrangement, 6(21), 203-214. (In Persian)
- Misaghi, F. (2016). Forecasting of the Alavian Dam Inflow Water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS). Iranian Journal of Soil and Water Research, 47(3), 439-448. (In Persian)
- Ruigar, H., & Golian, S. (2016). Prediction of precipitation in Golestan dam watershed using climate signals. Theoretical and applied climatology, 123(3), 671-682.
- Mbuvha, R., Jonsson, M., Ehn, N., & Herman, P. (2017, November). Bayesian neural networks for one-hour ahead wind power forecasting. In 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA)(pp. 591-596). IEEE.
- Mohammadi, M., Karami, H., Farzin, S., & Farokhi, A. (2017). Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station). Iranian Journal of Ecohydrology, 4(1), 201-214. (In Persian)
- Saligheh, M., & Sayadi, F. (2017). Summer precipitation determinant factors of Iran's South-East. Natural Environment Change, 3(1), 59-70. (In Persian)
- Steirou, E., Gerlitz, L., Apel, H., & Merz, B. (2017). Links between large-scale circulation patterns and streamflow in Central Europe: A review. Journal of Hydrology, 549, 484-500.
- Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., & Liu, X. (2017). Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resources Research, 53(4), 2786-2812.
- Alessandro, G. (2018). Informing water reservoir operations with climate teleconnections. Thesis.
- Kim, K., Lee, S., & Jin, Y. (2018). Forecasting quarterly inflow to reservoirs combining a copula-based Bayesian network method with drought forecasting. Water, 10(2), 233.
- Banihabib, M. E., Ahmadian, A., & Valipour, M. (2018). Hybrid MARMA-NARX model for flow forecasting based on large-scale climate signals, sea-surface temperatures, and rainfall. Hydrology Research, 49(6), 1788-1803.
- Ahmadi, M., Salimi, S., Hosseini, S. A., Poorantiyosh, H., & Bayat, A. (2019). Iran's precipitation analysis using synoptic modeling of major teleconnection forces (MTF). Dynamics of Atmospheres and Oceans, 85, 41-56.
- Babaei, M., Moeini, R., & Ehsanzadeh, E. (2019). Artificial neural network and support vector machine models for inflow prediction of dam reservoir (Case study: Zayandehroud dam reservoir). Water Resources Management, 33(6), 2203-2218.
- Samadi, M., & Fathabadi, A. (2019). Application of Time Series, ANN, and SVM Models in Forecasting the Gorgan Dam Inflow Rate. Environment and Water Engineering, 4(4), 299-309. (In Persian)
- Esmaili, K., Gandomkar, A., & Khodagholi, M. (2020). Identifying the Trend of Temperature Changes in the South Iranian Coasts and its Relationship with Teleconnections. Physical Geography Quarterly, 13(49), 1-22. (In Persian)
- Sabziparvar, A., Firoozmand, Z., & Varshavian, V. (2020). The Impact of Teleconnection Phenomena on Shifting the Date of First Autumn and Last Spring Frost Events: Physical Geography Research Quarterly, 52(2), 295-311. (In Persian)
- Maryanaji, Z., Tapak, L., & Hamidi, O. (2019). Climatic and atmospheric indices teleconnection impact the characteristics of frost season in western Iran. Journal of Water and Climate Change, 10(2), 391-401.
- Noorbeh, P., Roozbahani, A., & Kardan Moghaddam, H. (2020). Annual and monthly dam inflow prediction using Bayesian networks. Water Resources Management, 34(9), 2933-2951.
- Rasouli, K., Nasri, B. R., Soleymani, A., Mahmood, T. H., Hori, M., & Haghighi, A. T. (2020). Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snow cover and climate inputs. Hydrology Research, 51(3), 541-561.
- Wagena, M. B., Goering, D., Collick, A. S., Bock, E., Fuka, D. R., Buda, A., & Easton, Z. M. (2020). Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling & Software, 126, 104669.
- Wang, J., Wang, X., hui Lei, X., Wang, H., hua Zhang, X., jun You, J., feng Tan, Q., & lian Liu, X. (2020). Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition. Journal of Hydrology, 582, 124411.
- Zhang, X., Wang, H., Peng, A., Wang, W., Li, B., & Huang, X. (2020). Quantifying the uncertainties in data-driven models for reservoir inflow prediction. Water Resources Management, 34(4), 1479-1493.
- Lee, D., Kim, H., Jung, I., & Yoon, J. (2020). Monthly reservoir inflow forecasting for the dry period using teleconnection indices: a statistical ensemble approach. Applied Sciences, 10(10), 3470.
- Linh, N. T. T., Ruigar, H., Golian, S., Bawoke, G. T., Gupta, V., Rahman, K. U., Sankaran, A., & Pham, Q. B. (2021). Flood prediction based on climatic signals using wavelet neural network. Acta Geophysica, 69(4), 1413-1426.
- Latif, S. D., Ahmed, A. N., Sathiamurthy, E., Huang, Y. F., & El-Shafie, A. (2021). Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Natural Hazards, 109(1), 351-369.
- Panahi, F., Ehteram, M., Ahmed, A. N., Huang, Y. F., Mosavi, A., & El-Shafie, A. (2021). Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging. Ecological Indicators, 133, 108285.
- Behzadi, F., Javadi, S., Yousefi, H., & Moridi, A. (2022). Investigation and analysis of the effect of drought on groundwater aquifers in Iran (Case study: Shahrekord plain): Journal of Water and Irrigation Management, 12(2), 327-348. (In Persian)
- Chu, H., Bian, J., Lang, Q., Sun, X., & Wang, Z. (2022). Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information. Sustainability, 14(18), 11598.
- Helali, J., Ghaleni, M. M., Hosseini, S. A., Siraei, A. L., Saeidi, V., Safarpour, F., Mirzaei, M., & Lotfi, M. (2022). Assessment of machine learning model performance for seasonal precipitation simulation based on teleconnection indices in Iran. Arabian Journal of Geosciences, 15(15), 1-24.
- (2023). NOAA’s National Weather Service Climate Prediction Center. https://www.cpc.ncep.noaa.gov/data/teledoc/teleintro.shtml. Accessed 1/12/2023.
- (2023). NOAA’s National Weather Service Glossary. https://w1.weather.gov/glossary. Accessed 1/12/2023.
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