تحلیل خشکسالی هواشناسی با استفاده از الگوریتم بهینه‌سازی ازدحام ذرات- شبکه‌های عصبی مصنوعی بر مبنای شاخص MSPI

Asadnia M, Lioyd HC, Chau XS, Qin AM and Talei A, 2014. Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling. Journal of Hydrologic Engineering 19: 1320-1329.

Bacanli UG, Firat M and Dikbas F, 2009. Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment 23: 1143-1154.

Bazrafshan J, Hejabi S and Rahimi J, 2014. Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resources Management 28: 1045-1060.

Bazrafshan J, Nadi M and Ghorbani K, 2015. Comparison of empirical copula-based joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) for drought monitoring in Iran. Water Resources Management 29(6): 2027-2044.

Belayneh A, Adamowski J, Khalil B and Ozga-Zielinski B, 2014. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology 508: 418-429.

Chau KW, 2006. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology 329: 363-367.

Eberhart RC and Shi Y, 2000. Comparing inertia weights and constriction factors in particle swarm optimization. Pp. 84-88. Proceedings of the Congress on Evolutionary Computation, IEEE, La Jolla, California, USA.

Hassanzadeh Y, Abdi A and Fakheri-Fard A, 2012. Drought forecasting using genetic algorithm and conjoined model of neural network-wavelet. Water and Wastewater 23(83): 48-59.

Hassanzadeh Y, Abdi A, Shafiei M and Khoshtinat S, 2015. Daily streamflow forecasting of Nooranchay River using the hybrid model of artificial neural networks-principal component analysis. Water and Soil Science- University of Tabriz 25(3): 53-63.

Hassanzadeh Y, Abdi A, Talatahari S and Singh VP, 2011. Meta-heuristic algorithms for hydrologic frequency analysis. Water Resources Management 25(7): 1855-1879.

Kennedy J and Eberhart RC, 1995. Particle swarm optimization. Pp. 1942-1948. Proceedings of the 1995 IEEE International Conference on Neural Networks, IEEE Service Center, Perth, Australia.

Kim TW and Valdes JB, 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering 8(6): 319-328.

Mirjalili S, Mohd Hashim SZ and Sardroudi HM, 2012. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation 218: 11125-11137.

Mishra AK and Desai VR, 2006. Drought forecasting using feed-forward recursive neural network. Ecological Modelling 198: 127-138.

Morid S, Smakhtin V and Bagherzadeh K, 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology 27: 2103-2111.

Piotrowski AP and Napiorkowski JJ, 2011. Optimizing neural networks for river flow Forecasting-Evolutionary Computation methods versus the Levenberg-Marquardt approach. Journal of Hydrology 407: 12-27.

Rezaeian-Zadeh M and Tabari H, 2012. MLP-based drought forecasting in different climatic region. Theoretical Applied Climatology 109: 407-414.

Sharma S, 1996. Applied multivariate techniques. John Wiley & Sons, New York.

Shi Y and Eberhart R 1998. A modified particle swarm optimizer. Pp. 69-73. Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE Computer Society, Washington, USA.

Topoglou E, Trichakis IC, Doku Z, Nikolos IK and Karatsaz GP, 2014. Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization. Hydrological Sciences Journal 59(6): 1225-1239.

Wilks DS, 2011. Statistical methods in the atmospheric sciences, third edition, Academic Press, Amsterdam.

Zhang JR, Zhang J, Lok TM and Lyu MR, 2007. A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation 185: 1026-1037.