RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
Tài liệu tham khảo
Augusto, 2012, O contexto global e nacional frente aos desafios do acesso adequado à água para consumo humano, Cienc. e Saude Coletiva, 17, 1511, 10.1590/S1413-81232012000600015
Ministry of agriculture, 2002
Silva, 2014, Coeficientes de sensibilidade ao déficit hídrico para a cultura do girassol nas condições do semiárido cearense, Rev. Bras. Agric. Irrig., 8, 38, 10.1590/S1415-43662014000100006
Quilis, 2009, Measuring and modeling hydrological processes of sand-storage dams on different spatial scales, Phys. Chem. Earth, 34, 289, 10.1016/j.pce.2008.06.057
Nikolić, 2017, Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique, Knowl. Inf. Syst., 52, 255, 10.1007/s10115-016-1006-0
Petković, 2017, Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms, Flow Meas. Instrum., 54, 172, 10.1016/j.flowmeasinst.2017.01.007
Petković, 2017, Precipitation concentration index management by adaptive neuro-fuzzy methodology, Clim. Change, 141, 655, 10.1007/s10584-017-1907-2
Adnan, 2019, Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs, Arab. J. Geosci., 12, 1, 10.1007/s12517-019-4781-6
Jain, 1999, Application of ANN for reservoir inflow prediction and operation, J. Water Resour. Plan. Manag., 125, 263, 10.1061/(ASCE)0733-9496(1999)125:5(263)
Zasukhin, 2017, A technique for the calculation of evaporation from the soil surface based on moisture profiles, J. Comput. Syst. Sci. Int., 56, 420, 10.1134/S1064230717030145
Lindsey, 1997, Sources of solar radiation estimates and their effect on daily potential evaporation for use in streamflow modeling, J. Hydrol. (Amst), 201, 348, 10.1016/S0022-1694(97)00046-2
Kim, 2008, Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling, J. Hydrol. (Amst), 351, 299, 10.1016/j.jhydrol.2007.12.014
Shirgure, 2013, Evaporation estimations with neural networks, Evapotranspiration, 10.1201/b15779-8
Adnan, 2019, Comparison of LSSVR, M5RT, NF-GP, and NF-SC models for predictions of hourly wind speed and wind power based on cross-validation, Energies, 12, 329, 10.3390/en12020329
Adnan, 2019, Daily streamflow prediction using optimally pruned extreme learning machine, J. Hydrol., 577, 10.1016/j.jhydrol.2019.123981
Adnan, 2019, Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs, J. Hydrol., 124371
Adnan, 2019, Prediction of suspended sediment load using data-driven models, Water (Switzerland), 11, 2060
Jing, 2019
Moazenzadeh, 2018, Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran, Eng. Appl. Comput. Fluid Mech., 12, 584
Ali Ghorbani, 2018, Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran, Eng. Appl. Comput. Fluid Mech., 12, 724
Qasem, 2019, Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates, Eng. Appl. Comput. Fluid Mech., 13, 177
Salih, 2019, Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt, Eng. Appl. Comput. Fluid Mech., 13, 878
Moody, 1989, Learning in networks of locally-tuned processing units, Neural Comput., 1, 281, 10.1162/neco.1989.1.2.281
Bishop, 1995, Neural networks for pattern recognition, J. Am. Stat. Assoc.
Simon, 1999
Specht, 1991, A general regression neural network, Neural Networks, IEEE Trans., 2, 568, 10.1109/72.97934
Wachowiak, 2001, Generalized regression neural networks for biomedical image interpolation, Proceedings of the International Joint Conference on Neural Networks
Loukas, 2000, Radial basis function networks in host-guest interactions: instant and accurate formation constant calculations, Anal. Chim. Acta., 417, 221, 10.1016/S0003-2670(00)00934-X
Cobaner, 2009, Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data, J. Hydrol., 367, 52, 10.1016/j.jhydrol.2008.12.024
Qiu, 1999, Estimation of soil evaporation using the differential temperature method, Soil Sci. Soc. Am. J., 63, 1608, 10.2136/sssaj1999.6361608x
Kite, 2001, Modelling the mekong: hydrological simulation for environmental impact studies, J. Hydrol., 253, 1, 10.1016/S0022-1694(01)00396-1
Shahin, 2003
Amr, 2011, Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. Int. J. Phys. Sci., 6, 1306
El-Shafie, 2012, Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrol. Earth Syst. Sci., 16, 1151, 10.5194/hess-16-1151-2012
Maier, 2004, Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters, Environ. Model. Softw., 19, 485, 10.1016/S1364-8152(03)00163-4
Mohammadhassani, 2013, Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams, Struct. Eng. Mech., 46, 853, 10.12989/sem.2013.46.6.853
Shariat, 2018, Computational Lagrangian Multiplier Method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams, Steel Compos. Struct., 29, 243
Zhou, 2015, Relative importance analysis of a refined multi-parameter phosphorus index employed in a strongly agriculturally influenced watershed, Water Air Soil Pollut., 226