Almodfer, R., AbdElaziz, M., Abualigh, L., Mudsh, M., Shahzad, K., & Issa, M. (2022). Improving parameter estimating of fuel cell using honey badger optimization algorithm.
Frontiers in Energy Research, 10, 875332.
doi:10.3389/fenrg.2022.875332
Azad, A., Manoochehri, M., Kashi, H., Farzin, S., Karami, H., Nourani, V., & Shiri, J. (2019). Comparative evaluation of Intelligence algorithm to improve adaptive neuro fuzzy inference system performance in precipitation modelling.
Journal of Hydrology, 571, 214-224.
doi:10.1016/j.jhydrol.2019.01.062
Beheshti, Z., Firouzi, M., Shamsuddin, S.M., Zibarzani, M., & Yusop, Z. (2016). A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network. Neural Computing Applications, 27(8), 2551-2565. doi: 10.1007/s00521-015-2024-7
Cobaner, M., Citakoglu, H., Kisi, O., & Haktanir, T. (2014). Estimatin of mean monthly air temperatures in Turkey.
Computers and Electronics in Agriculture, 109, 71-79.
doi:10.1016/j.compag.2014.09.007
Danende Mehr, A., Nourani, V., Karimi Khosroshahi, V., & Ghorbani, M.A. (2019). A hybrid support vector regression – Firefly model for monthly rainfallforecasting. International Journal of Environmental Science and Technology, 16(1), 335-346. doi:10.1007/s13762-018-1674-2
Diop, L., Samadianfard, S., Bodian, A., Yassen, Z., Ghorbani, M.A., & Salimi, H. (2020). Annual rainfall forecasting using hybrid artificial intelligence model: integration of multilayer perceptron with whale optimization algorithm. Water Resources Management, 34(2), 733-746. doi:10.1007/s11269-019-02473-8
Hossain, I., Rasel, H.M., Imteaz, H.M., & Mekanik, F., (2020). Long-Term seasonal rainfall forecasting using linear and non-linear modeling approaches: a case study for Western Australia. Meteorology and Atmospheric Physics, 132(1), 31-141. doi:10.1007/s00703-019-00679-4
Maroufpoor, S., Bozorg-Hadded, O., & Maroufpoor, E. (2020). Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligentmodel. Hybrizidationof artificial neural network with grey wolf optimizer algorithm.
Journal of Hydrology, 588, 125060.
doi:10.1016/j.jhydrol.2020.125060
Maroufinia, E., Sharafati, A., Abgari, H., & Hassanzadeh, Y. (2023). The streamflow prediction of kurkusar river using hybrid artificial intelligence models with soft computing approach. Water and Soil Management, 3(1), 181-199.doi: 10..22098/mmws.2022.11657.1150. [In Persian]
Mirzania, E., Malek Ahmadi, H., Shahmohammadi, Y., & Ebrahimzadeh, A. (2021). Impact of wavelet on accuracy of estimated models in rainfall- runoff modeling (Case study: Sufi Chay). Water and Soil Management and Modeling, 1(3), 67-79. doi:10.22098/mmws.2021.9335.1035. [In Persian]
Mohammadi, B., Linh, N.T.T., Pham, Q.B., Ahmed, A.N., Vojtekova, J., Guan, Y., Abba, S.I., & El-Shafie, A. (2020). Adaptive neuro- fuzzy inference system coupled with frog leaping algorithm for predicting river streamflow time series.
Hydrological Sciences Journal 65, 1738-1751. doi:10.1080/02626667.2020.1758703
Nourani, V. (2017). An emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267-277. doi:10.1016/jhydrol.2016.11.033.
Paryani, S., Bordbar, M., Jun, C., Panahi, M., Bateni, S.M., Neale, C.M., Moeini, H., & Lee, S. (2022). Hybrid-basedapproachesfor the flood susceptibility prediction of Kermanshah province, Iran
. Natural Hazards,
116, 1-32.
doi:10.1007/s11069-022-05701-4
Poursalehi, F., Shahidi, A., & Khashei siuki, A. (2019). Comparison of decision tree M5 and K- nearest neighborhood algorithm models in the prediction of monthly precipitation (Case study: Birjand SynopticStation).
Iranian Journal of Irrigation & Drainage, 13(5), 1283-1293. dor:
20.1001.1.20087942.1398.13.5.9.3. [In Persian]
Rawa, M., Abusorrah, A., Al-Turki, Y., Calasan, M., Micev, M., Ali, Z.A., Mekhilef, S., Bassi, H., Sindi, H.S., Sindi, S., & Aleem, H.A. (2022). Estimating of parameters of different equivalent circuit models of solar cells and various photovoltaic modules using hybrid variants of Honey badger algorithm and artificial gorilla troops optimizer.
Mathematics, 10(7), 1057.
doi:10.3390/math10071057.
Rezaie Adaryani, F., Jamshid Mousavi, S., & Jafari, F. (2022). Short- term rainfall forecasting using machine learning- based approaches of PSO-SVR, LSTM and CNN.
Journal of Hydrology, 614, 128463.
doi:10.1016/j.jhydrol.2022.128463
Ridwan, W.M., Sapitang, M., Aziz, A., Kushiar, K.F., Ahmed, A.N., & El-Shafie, A. (2021). Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia,
Ain shams Engineering Journal, 12(2), 1651-1663.
doi:10.1016/j.asej.2020.09011
Samadianfard, S., & Asadi, E. (2017). Prediction of SPI drought index using support vector and multiple linear regressions.
Journal of Water and Soil Resources Conservation, 6(4), 1-16.
dor:10.1007//s40996-023-01068-z. [In Persian]
Saroughi, M., Mirzania, E., Vishwakarma, D.K., Nivesh, S., Panda, K.C., & Daneshvar, F.A. (2023). A novel Hybrid algorithm for groundwater level prediction.
Iranian Journal Science and Tecnology, Transaction of Civil Engineering. doi:10.1007//s40996-01068-z
Vahedi, N., Mashaiekhi, A., & Ghermezcheshme, B. (2023). Investigation the relation between agriculture and meteorological drought using multilayer perceptron (MLP) neural network in northwest Iran. Water and Soil Management and Modeling, 3(3), 163-173. doi:10.22098//mmws.2023.11847.1175. [In Persian]
Ye, L., Jabbar, F., Abdul Zahra, M.M., & Tan, M.L. (2021). Bayesian regularized neural network model development for predicting daily rainfall from sea level preesure data: Investigation on Solving Complex Hydrology Problem. Complexity, 1-14. doi:10.1155/2021/6631564
Zhou, D., Wang, S., Band, S., Mirzania, E., & Roshni, T. (2023). Atmosphere air temperature forecasting using the honey badger optimization algorithm: on the warmest and coldest areas of the world. Enginnering Applications of Computational Fluid Mechanics, 17(1), 2174189. doi:10.1080/1994060.2023.2174189
)