1.Abdolahnezhad, K. 2015. Forecasting of Monthly Sum-raining by Stochastic Models in Time Series. Geographical Planning of Space, 5: 17. 15-25.(In Persian)
2.Adnan, R.M., Yuan, X., Kisi, O., and Curtef, V. 2017. Application of time series models for streamflow forecasting. Civil and Environmental Research,9: 3. 56-63.
3.Ahmad, S., and Simonovic, S.P. 2005. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology, 315: 1-4. 236-251.
4.Akhtar, M.K., Corzo, G.A., Van Andel, S.J., and Jonoski, A. 2009. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin. Hydrology and Earth System Sciences, 13: 9. 1607-1618.
5.Arab, S., Khashei, S.A., Pourreza, B.M., and Hashemi, S.R. 2018. Comparison of Two Nonparametric Models, K-nearest neighbor and M5 Decision Tree in Forecasting the River Discharge in the Karaj Catchment. Watershed Management Research Journal. 30: 4. 47-58. (In Persian)
6.Asadi, H., Honarmand, M., Vazifedoust, M., and Mousavi, A. 2017. Assessment of Changes in Soil Erosion Risk Using RUSLE in Navrood Watershed, Iran. J. Agric. Sci. Tech. 19: 231-244.
7.Bashari, M., and Vatankhah, M. 2011. Comparison of different time series analysis methods for forecasting monthly discharge in Karkheh watershed. Irrigation and Water Engineering,1: 2. 75-86. (In Persian)
8.Bhattacharya, B., and Solomatine, D.P. 2005. Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing, 63: 381-396.
9.Cheng, C.T., Niu, W.J., Feng, Z.K., Shen, J.J., and Chau, K.W. 2015. Daily reservoir runoff forecasting methodusing artificial neural network basedon quantum-behaved particle swarm optimization. Water, 7: 8. 4232-4246.
10.Cryer, J.D. 1992. Time series Analysis. Translated by Niroomand, H.A., Mashhad University Publication, 404p. (In Persian)
11.Eshghi, P., Farzadmehr, J., Dastorani, M.T., and Arabasadi, Z. 2016. The effectiveness of intelligent models in estimating the river suspended sediments (Case Study: Babaaman Basin, Northern Khorasan. JWMR. 2017; 7: 14. 95-88. (In Persian)
12.Esmaeili, H., Akhond Ali, A.M., Zarei, H., and Taghian, M. 2018. Regional Flood Analysis Via Comparison of
The M5 Decision Tree Algorithmand Regression Models. Irrigation Sciences and Engineering, 40: 4. 183-195. (In Persian)
13.Fathi, P., Mohammadi, Y., and Homaei, M. 2009. Intelligent modeling of monthly flow time series into vahdat dam in sanandaj city. Journal of Water and soil, 21: 1. 209-220. (In Persian)
14.Ghorbani, K., Naeimi Kalourazi, Z., Salarijazi, M., and Dehghani, A.A. 2016. Estimation of monthly discharge using climatological and physiographic parametesr of ungauged basin. Journal of Water and Soil Conservation,23: 3. 207-224. (In Persian)
15.Ghorbani, K., Sohrabian, E., and Salarijazi, M. 2016. Evaluation of hydrological and data mining models in monthly river discharge simulation and prediction (Case study: Araz-Kouseh watershed). Journal of Water and Soil Conservation, 23: 1. 203-217. (In Persian)
16.Hadizadeh, R., Eslamian, S., and Chinipardaz, R., 2013, Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran’, Int. J. Hydrology Science and Technology, 3: 4. 319-350.
17.Haghizadeh, A., Mohammadlou, M., and Noori, F. 2015. Simulation of rainfall-runoff process using multilayer perceptron and adaptive neuro-fuzzy interface system and multiple regression (Case Study: Khorramabd Watershed. Iranian journal of Ecohydrology,2: 2. 233-243. (In Persian)
18.Jandaghi, N., Azimmohseni, M., and Ghareh Mahmoodlu, M. 2021. Rainfall-runoff process modeling using time series transfer function. Environmental Erosion Research Journal, 11: 2. 111-128. (In Persian)19.Kang, K.W., Kim, J.H., Park, C.Y.,
and Ham, K.J. 1993. Evaluation of hydrologic forecasting system based on neural network model. In Proceedings of the Congress-international Association for research. 1: 257-257.
20.Khodakhah, H., Aghelpour, P., and Hamedi, Z. 2022. Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH. Environmental Science and Pollution Research, 29: 15. 21935-21954.
21.Kia, I., Emadi, A.R., and Gholami, M. 2019. Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR). Irrigation and Water Engineering, 9: 4. 39-51. (In Persian)
22.Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M. 2018. Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22: 11. 6005-6022.
23.Krstanovic, P.F., and Singh, V.P. 1991. A univariate model for long-term streamflow forecasting. Stochastic hydrology and hydraulics, 5: 3. 189-205.
24.Laux, P., Vogl, S., Qiu, W., Knoche, H. R., and Kunstmann, H. 2011. Copula-based statistical refinement of precipitation in RCM simulations over complex terrain. Hydrology and Earth System Sciences, 15: 7. 2401-2419.
25.Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M., and Francis, R.C.1997. A Pacific interdecadalclimate oscillation with impacts on salmon production. Bulletin of the american Meteorological Society,78: 6. 1069-1080.
26.Masoumpour Samakosh, J., Jalilian, A., and Yari, E. 2017. The analysis of seasonal precipitation time series in Iran. Physical Geography Research Quarterly, 49: 3. 457-475. (In Persian)
27.Mirzapour, H., Haghizadeh, A., and Alijani, R. 2018. The Evaluation of the Performance of SARIMA Time Series Model in the Simulation of the Average Monthly Discharge of the Kashkan Afrineh and Kakareza Rivers (Lorestan province). Hydrogeomorphology,4: 55. 153-169. (In Persian)
28.Naeimi Kalourazi, Z., Kh, G.,Salarijazi, M., and Dehghani, A.A. 2016. Estimation of monthly discharge using climatic and physiographic parameters of ungauged basins. Journal of Water and Soil Conservation,23: 3. 207-224. (In Persian)
29.Panahi, A., and AIijani, B. 2013. Forcasting Peek Flood In The Madarsoo Basin Using Neural Network And Variable Several Regressions Method (Case Study: Madar Soo Basin). Geography, 11: 38. 113-132. (In Persian)
30.Poul, A.K., Shourian, M., and Ebrahimi, H. 2019. A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resources Management, 33: 8. 2907-2923.
31.Salahi, B., and Sarmasti, T. 2014. Simulation of Runoff-runoff presses in southern Sub-basin of Gharesou by Artificial neural networks mode (ANNS). Geography and environmental planning, 24: 4. 119-134. (In Persian)
32.Salarijazi, M., Ghorbani, K., Sohrabian, E., and Abdolhosseini, M. 2016. Prediction of Daily Stream-flow Using Data Driven Models. Iranian Journal of Irrigation & Drainage, 10: 4. 479-488. (In Persian)
33.Samadi, M., Bahremand, A., and Fathabadi, A. 2020. The Boustan Dam monthly inflow forecasting usingdata-driven and ensemble models inthe Golestan Province, Watershed Engineering and Management,11: 4. 1044-1058. (In Persian)
34.Solomatine, D.P., and Dulal, K.N. 2003. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal,48: 3. 399-411.
35.Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S. 2002. A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models. Hydrological processes, 16: 6. 1325-1330.
36.Talebi, A., and Akbari, Z. 2013. Investigation of ability of decision trees model to estimate river suspended sediment (case study: Ilam Dam basin). Journal of Science and Technology of Agriculture and Natural Resources,17: 63. 109-121. (In Persian)
37.Valipour, M., Banihabib, M.E., and Behbahani, S.M.R. 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of hydrology, 476: 433-441.
38.Vyas, S.K., Mathur, Y.P., Sharma, G., and Chandwani, V. 2016. December. Rainfall-Runoff Modelling: Conventional regression and Artificial Neural Networks approach. In 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE.
39.Wang, W.C., Chau, K.W., Xu, D.M., and Chen, X.Y. 2015. Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management, 29: 8. 2655-2675.
40.Witten, I.H., and Frank, E. 2002. Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31: 1. 76-77.
41.Wood, E.F., and Rodríguez‐Iturbe, I. 1975. Bayesian inference and decision making for extreme hydrologic
events. Water Resources Research,11: 4. 533-542.
42.Wu, C.L., and Chau, K.W. 2010. Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23: 8. 1350-1367.
43.Zahiri, A.R., and Ghorbani, K.H. 2013. Flow discharge prediction in compound channels by using decision model
tree M5. Journal of Water andSoil Conservation, 20: 3. 113-132.(In Persian)