عنوان مقاله [English]
Recently, the amount of rainfall underwent serious changes in different areas, particularly in arid and semi-arid regions. Therefore, estimation and pattern recognition of rainfall in a long term period can give sufficient information to hydrologists and water engineers. In this study, for the first time, long-term rainfall pattern in Anzali city for a 67 years period was simulated using a hybrid model so-called “Wavelet-Adaptive Neuro-Fuzzy Inference System” (WANFIS). Rainfalls of 37-, 20- and 10-years period were applied for training, testing and validation of the numerical model, respectively. Firstly, the optimized membership function of the ANFIS network was obtained using the analysis of the numerical results. In other words, the number of optimized membership function was computed to be equal to 8. Then, the various wavelet families were evaluated which the dmey mother wavelet was introduced as the most optimized wavelet family. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WANFIS models were developed. According to the sensitivity analysis, the superior model and effective lags were identified. The superior model estimated the rainfall with high accuracy. For instance, for validation mode of the superior model, the correlation coefficient, scatter index and Nash-Sutcliffe efficiency coefficient were computed to be 0.962, 0.258 and 0.899, respectively.
Abhishek, K., Kumar, A., Ranjan, R. and Kumar, S. (2012). A rainfall prediction model using artificial neural network. In Control and System Graduate Research Colloquium (ICSGRC), IEEE (82-87). IEEE.
Akrami, S.A., Nourani, V. and Hakim, S.J.S. (2014). Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water resources management, 28(10), 2999-3018.
Aksoy, H. and Dahamsheh, A. (2009). Artificial neural network models for forecasting monthly precipitation in Jordan. Stochastic Environmental Research and Risk Assessment, 23(7), 917-931.
Cramer, S., Kampouridis, M., Freitas, A. A. and Alexandridis, A. (2019). Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives. Swarm and Evolutionary Computation, 46, 184-200.
Danladi, A., Stephen, M., Aliyu, B. M., Gaya, G. K., Silikwa, N. W. and Machael, Y. (2018). Assessing the influence of weather parameters on rainfall to forecast river discharge based on short-term. Alexandria Engineering Journal, 57(2), 1157-1162.
Dehghani, N., Vafakhah, M. and Bahremand, E. (2016). Modeling of precipitation-runoff using artificial intelligence network and adaptive neuro-fuzzy inference network in Kasilian basin. Journal of watershed management, seventh year. 13, 128-137. (in Persian)
Hardwinarto, S., and Aipassa, M. (2015). Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong Station, East Kalimantan-Indonesia. Procedia Computer Science, 59, 142-151.
Jang, J.S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Khalili, N., Khodashenas, S.R., Davari, K. and Mousavi Baygi, M. (2008). Prediction of daily precipitation using artificial intelligence networks, Case study: Mashhad synoptic station. Watershed studies, 89-99. (in Persian)
Khosravi, M. and Shakiba, H. (2010). Prediction of precipitation using artificial intelligence networks in order to flood management: Case study: Iranshahr district. Fourth international congress of Islam world geographers. Zahedan, Iran. (in Persian)
Lee, S., Cho, S. and Wong, P.M. (1998). Rainfall prediction using artificial neural networks. journal of geographic information and Decision Analysis, 2(2), 233-242.
Maqsood, I., Khan, M.R. and Abraham, A. (2004). An ensemble of neural networks for weather forecasting. Neural Computing & Applications, 13(2), 112-122.
Nourani, V., Hosseini Baghanam, A., Adamowski, J. and Kisi, O. (2014). Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514, 358–377.
Purnomo, H.D., Hartomo, K.D. and Prasetyo, S.Y.J. (2017). Artificial neural network for monthly rainfall rate prediction. In IOP Conference Series: Materials Science and Engineering, 180: 1. 012057. IOP Publishing.
Ramirez, M.C.V., de Campos Velho, H.F. and Ferreira, N.J. (2005). Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of hydrology, 301(1-4), 146-162.
Riad, S., Mania, J., Bouchaou, L. and Najjar, Y. (2004). Rainfall-runoff model usingan artificial neural network approach. Mathematical and Computer Modelling, 40(7-8), 839-846.
Toth, E., Brath, A. and Montanari, A. (2000). Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of hydrology, 239(1-4), 132-147.
Wong, K.W., Wong, P.M., Gedeon, T.D. and Fung, C.C. (2003). Rainfall prediction model using soft computing technique. Soft Computing, 7(6), 434-438.
Wong, K.W., Wong, P.M., Gedeon, T.D. and Fung, C.C. (1999). Rainfall prediction using neural fuzzy technique. URL: www. it. murdoch. edu. au/~ wong/publications/SIC97. 213-221.