Abedini, Z. (2022). Places to visit in Ferdows, Travel guide to Ferdows. Rahbal Blog. Retrieved May14,2025, from https://blog.rahbal.com/ferdows/
Ahmadi Shali, J. & Vasfi, M. (2017). Liquidity forecasting based on point and interval estimation of the ARIMA method and its comparison with the double exponential smoothing method. Financial Economics (Financial Economics and Development), 11(40), 159-175. [in Persian]
Al Balasmeh, O., Babbar, R., & Karmaker, T. (2019). Trend analysis and ARIMA modeling for forecasting precipitation pattern in Wadi Shueib catchment area in Jordan. Arabian Journal of Geosciences, 12, 1-19. https://doi.org/10.1007/s12517-018-4205-z.
Al Sayah, M. J., Abdallah, C., Khouri, M., Nedjai, R., & Darwich, T. (2021). A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling. Theoretical and Applied Climatology, 143, 639-658. https://doi.org/10.1007/s00704-020-03442-7
Brockwell, P. J., & Davis, R. A. (1991). Time series: theory and methods. Springer science & business media. https://doi.org/10.1007/0-387-21657-x_11
Daniel, E. B., Camp, J. V., LeBoeuf, E. J., Penrod, J. R., Dobbins, J. P., & Abkowitz, M. D. (2011). Watershed modeling and its applications: A state-of-the-art review. Open Hydrology Journal, 5(1), 26-50. https://doi.org/10.2174/1874378101105010026
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23(4), 586-594. https://doi.org/10.1016/j.engappai.2009.09.015
Ghafourian, H., Sanaei Nejad, S. H., and Jabbari Nooghabi, M. (2020). Evaluation of Time Series Models in Prediction of Seasonal Precipitation Based on Remote Sensing Data (Case Study: Arid and Semi-arid Climates). Journal of Climate Research, 1399(42), 77-94. https://clima.irimo.ir/article_125162.
Loudyi, D., Falconer, R. A., & Lin, B. (2007). Mathematical development and verification of a non-orthogonal finite volume model for groundwater flow applications. Advances in water resources, 30(1), 29-42. https://doi.org/10.1016/j.advwatres.2006.02.010
Hassanpour, M., & Khozaymehnejad, H. (2017). Determination of suitable areas for artificial recharge to increase qanat discharge in the Gonabad aquifer. Journal of Auifer and Qanat, 1(1), 13-25. . https://doi.org/10.5194/egusphere-egu22-2236 [In Persian]
Imani, R. I. , Ghazavi, R. and Esmali Ouri, A. (2021). Stochastic Monthly Rainfall Time Series Analysis, Modeling and Forecasting ( A case study: Ardebilcity. Journal of Arid Regions Geographic Studies,12(44),84-98. [in Persian]
Khayat, A., Akhondi, Z., & Khozeymehnezhad, H. (2025). Evaluation of the accuracy of fuzzy neural network in estimating the discharge of Qanats in Birjand city. Journal of Aquifer and Qanat. https://doi.org/10.5040/9780755650828.0007
Marofi, S., Khetar, B., Sadeghifar, M., Parsafar, N., & Ildoromi, A. (2013). Drought prediction using SARIMA time series and SPI index in the central region of Hamedan province. Water Research in Agriculture, 28(1), 213-225.
Sattari, M., Shamsi Sosahab, R. (2014).Estimation of Groundwater Level in Ardebil Plain Using Artificial Neural Networks. Pages 1-7. 11th National Students Conference. University of Urmia, Urmia, September 11-13. https://doi.org/10.1111/gwat.12620
Wang, W. C., Chau, K. W., Xu, D. M., & Chen, X. Y. (2015). Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water resources management, 29, 2655-2675. https://doi.org/10.1007/s11269-015-0962-6
Younesi, H., Torabi Podeh, H., Arshiya, A., & Mirzapour, H. (2017). Simulation of average monthly flow of Badavar-Noorabad River using time series models, 6th Scientific Research Conference on Soil Resources Management, Kerman. [in Persian]