Aghakhani Afshar, A., Hassanzadeh, Y., Besalatpour, A. A., & Pourreza Bilondi, M. (2017). Annual assessment of Kashafrood watershed basin climate components in future periods by using fifth report of intergovernmental panel on climate change. Journal of Water and Soil Conservation, 23(6), 217-233,
https://doi.org/10.22069/jwfst.2017.11186.2553. (in Persian)
Alizamir, M., Kisi, O., Ahmed, A.N., Mert, C., Fai, C.M., Kim, S., Kim, N.W. and El-Shafie, A., )2020(. Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One, 15(4), p.e0231055,
https://doi.org/10.1371/journal.pone.0231055.
Astsatryan, H., Grigoryan, H., Poghosyan, A., Abrahamyan, R., Asmaryan, S., Muradyan, V., ... & Giuliani, G. (2021). Air temperature forecasting using artificial neural network for Ararat valley. Earth Science Informatics, 14, 711-722,
https://doi.org/10.1007/s12145-021-00583-9.
Azari, A., Zeynoddin, M., Ebtehaj, I., Sattar, A. M., Gharabaghi, B., & Bonakdari, H. (2021). Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica, 69(4), 1395-1411,
https://doi.org/10.1007/s11600-021-00617-2.
Azari, B., Hassan, K., Pierce, J., & Ebrahimi, S. (2022). Evaluation of machine learning methods application in temperature prediction. Environ Eng, 8, 1-12,
https://doi.org/10.52547/crpase.8.1.2747.
Ebtehaj, I., Bonakdari, H., & Shamshirband, S. (2016). Extreme learning machine assessment for estimating sediment transport in open channels. Engineering with Computers, 32, 691-704,
https://doi.org/10.1007/s00366-016-0446-1.
Ebtehaj, I., Bonakdari, H., Zeynoddin, M., Gharabaghi, B., & Azari, A. (2020). Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models. International Journal of Environmental Science and Technology, 17, 505-524,
https://doi.org/10.1007/s13762-019-02361-z.
Esmaeili, F., Shabanlou, S., & Saadat, M. (2021). A wavelet-outlier robust extreme learning machine for rainfall forecasting in Ardabil City, Iran. Earth Science Informatics, 14(4), 2087-2100,
https://doi.org/10.1007/s12145-021-00681-8.
Feigl, M., Lebiedzinski, K., Herrnegger, M., & Schulz, K. (2021). Machine-learning methods for stream water temperature prediction. Hydrology and Earth System Sciences, 25(5), 2951-2977,
https://doi.org/10.5194/hess-25-2951-2021.
Huang, G. B., & Siew, C. K. (2004, December). Extreme learning machine: RBF network case. In ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004. (Vol. 2, pp. 1029-1036). IEEE,
https://doi.org/10.1109/ICARCV.2004.1468985.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501,
https://doi.org/10.1016/j.neucom.2005.12.126.
Inard, C., Bouia, H., & Dalicieux, P. (1996). Prediction of air temperature distribution in buildings with a zonal model. Energy and buildings, 24(2), 125-132,
https://doi.org/10.1016/03787788(95)00969-8.
IPCC, (2013),Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp,
https://doi.org/10.1017/CBO978110741532
IPCC. (2001), Special Report on Emission Scenarios. N. Nakicenovic, R. Swart (Eds). Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK.
Jusuf, S. K., & Wong, N. H. (2009). Development of empirical models for an estate level air temperature prediction in Singapore. In Proceedings of the second international conference on countermeasures to urban heat islands (pp. 21-23).
Masoodian, S. A. (2004). Temperature trends in Iran during the last half century. Geography and Development, 2(3), 89-106. (in Persian)
Masoompoor Samakoosh, J; Sohrabi, V; & Miri, M. (2022), Evaluation and prediction of droughts in the we.st and northwest of Iran using artificial neural network. Journal of Geographical Studies of Mountainous Areas, 3(10), 19-37, https://doi.org/ 10.52547/gsma.3.2.19. (in Persian)
Mohammed, K. S., Shabanlou, S., Rajabi, A., Yosefvand, F., & Izadbakhsh, M. A. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Applied Water Science, 13(2), 54,
https://doi.org/10.1007/s13201-022-01861-7.
Pandey, P., & Govind, R. (2016). Analysis of randomized performance of bias parameters and activation function of extreme learning machine. International Journal of Computer Applications, 135(1), 23-28.
Roy, D. S. (2020). Forecasting the air temperature at a weather station using deep neural networks. Procedia Computer Science, 178, 38-46,
https://doi.org/10.1016/j.procs.2020.11.005
Salcedo-Sanz, S., Deo, R. C., Carro-Calvo, L., & Saavedra-Moreno, B. (2016). Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and applied climatology, 125, 13-25,
https://doi.org/10.1007/s00704-015-1480-4.
Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., & Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of the Total Environment, 770, 145288,
https://doi.org/10.1016/j.scitotenv.2021.145288.
Swaid, H., & Hoffman, M. E. (1990). Prediction of urban air temperature variations using the analytical CTTC model. Energy and Buildings, 14(4), 313-324,
https://doi.org/10.1016/03787788(90)90094-Y.
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of geophysical research: atmospheres, 106(D7), 7183-7192,
https://doi.org/10.1029/2000JD900719.
Yang, J., & Zhang, Y. (2011). Alternating direction algorithms for \ell_1-problems in compressive sensing. SIAM journal on scientific computing, 33(1), 250-278,
https://doi.org/10.1137/090777761.
Zeynoddin, M., Bonakdari, H., Azari, A., Ebtehaj, I., Gharabaghi, B., & Madavar, H. R. (2018). Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate. Journal of environmental management, 222, 190-206,
https://doi.org/10.1016/j.jenvman.2018.05.072.
Zeynoddin, M., Bonakdari, H., Ebtehaj, I., Azari, A. and Gharabaghi, B. 2020. A generalized linear stochastic model for lake level prediction. Science of The Total Environment. 723, 138015, https://doi.org/10.1016/j.scitotenv.2020.138015
