ﺑﺨﺘﯿﺎری، ب.، ﺷﻬﺮﮐﯽ، ن. و اﺣﻤﺪی، م. م. 1392. ﺑﺮآورد اﺣﺘﻤﺎﻻت ﺑﺎرش روزاﻧﻪ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل زﻧﺠﯿﺮه ﻣﺎرﮐﻒ در اﻗﻠﯿﻢ ﻫﺎی ﻣﺨﺘﻠﻒ اﯾﺮان. ﺗﺤﻘﯿﻘﺎت ﻣﻨﺎﺑﻊ آب اﯾﺮان. 2 (10): 55-44.
ﺳﺘﺎری، م.ت.، رﺿﺎزاده ﺟﻮدی، ع. و ﻧﻬﺮﯾﻦ ، ﻓﺮﻧﺎز. 1393. ﭘﯿﺶ ﺑﯿﻨﯽ ﻣﻘﺎدﯾﺮ ﺑﺎرش ﻣﺎﻫﺎﻧﻪ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺒﮑﻪ ﻫﺎی ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ و ﻣﺪل درﺧﺘﯽ M5 ﻣﻄﺎﻟﻌﻪ ﻣﻮردی: اﯾﺴﺘﮕﺎه اﻫﺮ. ﭘﮋوﻫﺶ ﻫﺎی ﺟﻐﺮاﻓﯿﺎی ﻃﺒﯿﻌﯽ. 46 (2): 260-247.
ﻓﻼﺣﯽ، م. ر.، ورواﻧﯽ، ه. وﮔﻠﯿﺎن، س. 1390. ﭘﯿﺶ ﺑﯿﻨﯽ ﺑﺎرش ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل رﮔﺮﺳﯿﻮن درﺧﺘﯽ ﺑﻪ ﻣﻨﻈﻮر ﮐﻨﺘﺮل ﺳﯿﻞ. ﭘﻨﺠﻤﯿﻦ ﮐﻨﻔﺮاﻧﺲ ﺳﺮاﺳﺮی آﺑﺨﯿﺰداری و ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ آب و ﺧﺎک ﮐﺸﻮر.
Adamowski, K., Prokoph, A. and Adamowski, J. 2009. Development of a new method of wavelet aided trend detection and estimation. Hydrology Process. 23(18): 2686-2696.
Amirat, Y., Benbouzidb, M., Wang, T., Bacha, K. and Feld, G. 2018. EEMD-based notch filter for induction machine bearing faults detection. Applied Acoustics. 133: 202-209.
ASCE. 2000. Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in hydrology. I: Preliminary concepts. Hydrological Engineering, ASCE. 5(2): 115-123.
Chong, K. L., Lai, S. H., Yao, Y., Ahmed, A. N., Jaafar, W. Z. and El-Shafie, A. 2020. Performance enhancement model for rainfall forecasting utilizing integrated wavelet-convolutional neural network. Water Resources Management. 34(8): 2371-2387.
Chou, C. M. 2011. Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research and Risk Assessment. 6: 1401-1408.
Deo, R. C., Samui, P. and Kim, D. 2016. Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environmental Research and Risk Assessment. 30: 1769-1786.
Kisi, O., Cimen, M. 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Application Artificial Intelligence. 25: 783-792.
Kumar, S., Tripathy, D., Nayak, S., Mohaparta, S. 2013. Prediction of rainfall in India using artificial neural network models. International Journal of intelligent system and applications. 12: 1-22.
Lau, K. M. and Weng, H. Y. 1995. Climate signal detection using wavelet transform, How to make time series sing. Bull Am Meteorol Soc. 76: 2391-2402.
Marzano, F. S., Fionda, E. and Ciotti, P. 2006. Neural-network approach to ground- based passive microwave estimation of precipitation intensity and extinction. Hydrology. 328: 121-131.
Nayak, D., Mahapatra, A. and Mishra, P. 2013. A survey on rainfall prediction using artificial neural network. International journal of computer applications. 72(16): 32-40.
Roushangar, K. and Ghasempour, R. 2017. Estimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithm. International Journal of Sediment Research. 32(4): 564-574.
Samantaray, S., Tripathy, O., Sahoo, A. and Ghose, D. K. 2020. Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. In smart intelligent computing and applications (pp. 767-774). Springer, Singapore.
Siviapragasam, C. and Liong, S. 2001. Rainfall and runoff forcasting with SSA-SVM approach. Hydroinformation. 3: 141-152.
Soltani, A. S., Saberi, A. and Gheisouri, M. 2017. Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province. Researches in Geographical Sciences. 17(44): 87-105.
Wu, Z. and Huang, N. F. 2004. A study of the characteristics of white noise using the empirical mode decomposition method. Proc RS Lond 460A: 1597-1611.