Abdel-Aal, R., Elhadidy, M. 1994. A machine-learning approach to modelling and forecasting the minimum temperature at Dhahran, Saudi Arabia. Energy, 19(7), 739-749.
Allen, C. C. 1957. A simplified equation for minimum temperature prediction. Monthly weather review, 85(4), 119-120.
Asadi, H., Shahedi, K., Sidle, R. C., Kalami Heris, S. M. 2019. Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models. Iran-Water Resources Research, 15(3), 105-119.
Azizi, G., Roshani, M. 2008. Using Mann-Kendall Test to Recognize of Climate Change in Caspian Sea Southern Coasts. Physical Geography Research Quarterly, 64, 13-28.
Benavides, R., Montes, F., Rubio, A., Osoro, K. 2007. Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology, 146(3-4), 173-188.
Brunt, D. 2011. Physical and dynamical meteorology, Cambridge University Press.
Dodson, R., Marks, D. 1997. Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate research, 8(1), 1-20.
Dombaycı, Ö. A., Gölcü, M. 2009. Daily means ambient temperature prediction using artificial neural network method, A case study of Turkey. Renewable Energy, 34(4), 1158-1161.
Ferreira, C. 2001. Gene expression programming, a new adaptive algorithm for solving problems. Complex systems, 13.
Figuerola, P., Mazzeo, N. 1997. An analytical model for the prediction of nocturnal and dawn surface temperatures under calm, clear sky conditions. Agricultural and Forest Meteorology, 85(3-4), 229-237.
George, R. K. 2001. Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Analysis, Theory, Methods Applications, 47(3), 1737-1748.
Ghorbani, M., Shiri, J., Kazemi, H. 2010. Estimation of Maximum, Mean and Minimum Air Temperature in Tabriz City Using Artificial Intelligent Methods. Water and Soil Science, 20(3), 87-104. (In Farsi)
Hakimi Khansar, H., Parsa, J., Hosseinzadeh Dalir, A., Shiri, J. 2021. Pore water pressure coefficient forecasting in the body of earth dams at the time of construction and determination of its effective features using WCA-ANN hybrid algorithm. Dam and Hydroelectric Powerplant, 8(29), 26-39. (In Farsi)
Hakimi Khansar, h., Shabani Chafjiri, a. 2021. Determining Effective Features for Estimating the Volume of Water Delivered to the Irrigation and Drainage Network using Artificial Intelligence Methods (Case study: Irrigation and Drainage Network of Sefidrood Dam). Water Management in Agriculture, 8(2), 117-134. (In Farsi)
Hudson, G., Wackernagel, H. 1994. Mapping temperature using kriging with external drift, theory and an example from Scotland. International journal of Climatology, 14(1), 77-91.
Jaeger, J. 1945. Note on the effect of wind on nocturnal cooling. Quarterly Journal of the Royal Meteorological Society, 71(309-310), 388-390.
Jang, J.-S. 1993. ANFIS, adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. doi,10.1109/21.256541.
Jang, J., Sun, C., Mizutani, E. Neuro-fuzzy and Software Computing, A Computational Approach to Learning and Machine Intelligence, 1997, Prentice-Hall, Englewood Cliffs, NJ.
Kişi, Ö., Öztürk, Ö. 2007. Adaptive neurofuzzy computing technique for evapotranspiration estimation. Journal of Irrigation and Drainage Engineering, 133(4), 368-379.
Li, M.-F., Tang, X.-P., Wu, W., Liu, H.-B. 2013. General models for estimating daily global solar radiation for different solar radiation zones inmainland China. Energy Conversion and Management, 70, 139-148.
Moghaddamnia, A., Gousheh, M. G., Piri, J., Amin, S., Han, D. 2009. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), 88-97.
Norouzi, R., Sihag, P., Daneshfaraz, R., Abraham, J., Hasannia, V. 2021. Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques. Water Supply, 32(2), 80-95.
Parsa, J., Hakimi Khansar, H., Hoseinzadeh dalir, A., Shiri, J. 2021. Simulation of soil stress in earth dams using artificial intelligence models and determination of effective features. Amirkabir Journal of Civil Engineering, 54(1), 247-262. (In Farsi)
Paruelo, J., Tomasel, F. 1997. Prediction of functional characteristics of ecosystems, a comparison of artificial neural networks and regression models. Ecological Modelling, 98(2-3), 173-186.
Patterson, D. W. 1998. Artificial neural networks, theory and applications, Prentice Hall PTR.
Robinson, C., Mort, N. 1997. A neural network system for the protection of citrus crops from frost damage. Computers and electronics in agriculture, 16(3), 177-187.
Rubio Sánchez, A., Sánchez Palomares, O., Gómez, V., Graña, D., Elena Rosselló, R., Blanco Andray, A. 2002. Autoecología de los castañares de Castilla (España). Investigación Agraria, Sistemas y Recursos Forestales, 11(2), 373-393.
Smith, B. A., Hoogenboom, G., McClendon, R. W. 2009. Artificial neural networks for automated year-round temperature prediction. Computers and electronics in agriculture, 68(1),52-61.
Soares, J., Oliveira, A. P., Božnar, M. Z., Mlakar, P., Escobedo, J. F., Machado, A. J. 2004. Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique. Applied energy, 79(2), 201-214.
Ustaoglu, B., Cigizoglu, H., Karaca, M. 2008. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorological Applications, A journal of forecasting, practical applications, training techniques and modelling, 15(4), 431-445.
Zuzel, J. F., Cox, L. M. 1975. Relative importance of meteorological variables in snowmelt. Water resources research, 11(1), 174-176.
