[1]
S. Tohma, S. Igata, S. Rainfall estimation from GMS imagery data using neural network. WIT Trans. on Ecol. and the Envir. 7 (1970).
Google Scholar
[2]
H.D. Navone, H.A. Ceccatto. Predicting Indian monsoon rainfall: a neural network approach. Clim. Dynam. 10(6-7) (1994) 305-312.
DOI: 10.1007/bf00228029
Google Scholar
[3]
H. Raman, N. Sunilkumar. Multivariate modelling of water resources time series using artificial neural networks. Hydr. Sci. J. 40(2) (1995) 145-163.
DOI: 10.1080/02626669509491401
Google Scholar
[4]
A.H. Halff, H.M. Halff, M. Azmoodeh. Predicting runoff from rainfall using neural networks. In Eng. Hydr. (1993) 760-765.
Google Scholar
[5]
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial neural networks in hydrology. II: Hydrologic applications. J. of Hydr. Eng. 5(2) (2000) 124-137.
DOI: 10.1061/(asce)1084-0699(2000)5:2(124)
Google Scholar
[6]
M.L. Zhu, M. Fujita, N. Hashimoto. Application of neural networks to runoff prediction. In Stochastic and statistical methods in hydrology and environmental engineering (1994) 205-216.
DOI: 10.1007/978-94-017-3083-9_16
Google Scholar
[7]
J. Smith, R.N. Eli. Neural-network models of rainfall-runoff process. J. of wat. res. plan. and manag. 121(6) (1995) 499-508.
Google Scholar
[8]
P. Carriere, S. Mohaghegh, R. Gaskari. Performance of a virtual runoff hydrograph system. J. of wat. res. plan. and manag. 122(6) (1996) 421-427.
DOI: 10.1061/(asce)0733-9496(1996)122:6(421)
Google Scholar
[9]
D.N. Kumar, K.S. Raju, T. Sathish. River flow forecasting using recurrent neural networks. Wat. res. manag., 18(2) (2004) 143-161.
DOI: 10.1023/b:warm.0000024727.94701.12
Google Scholar
[10]
N. Karunanithi, W.J. Grenney, D. Whitley, K. Bovee. Neural networks for river flow prediction. J. of comp. in civ. eng. 8(2) (1994) 201-220.
DOI: 10.1061/(asce)0887-3801(1994)8:2(201)
Google Scholar
[11]
M. Markus, J.D. Salas, H.S. Shin. Predicting streamflows based on neural networks. In Proc. of the 1st Int. Conf. on Wat. Res. 1 (1995) 1641-1646.
Google Scholar
[12]
M. Tawfik, A. Ibrahim, H. Fahmy, H. Hysteresis sensitive neural network for modeling rating curves. J. of Comp. in Civ. Eng. 11(3) (1997) 206-211.
DOI: 10.1061/(asce)0887-3801(1997)11:3(206)
Google Scholar
[13]
H.R. Maier, G.C. Dandy. The use of artificial neural networks for the prediction of water quality parameters. Wat. res. res. 32(4) (1996) 1013-1022.
DOI: 10.1029/96wr03529
Google Scholar
[14]
N. Sandhu, R. Finch. Emulation of DWRDSM using artificial neural networks and estimation of Sacramento River flow from salinity. In North Amer. Wat. and Env. Cong. & Dest. Wat. (1996) 4335-4340.
Google Scholar
[15]
P.H. Hutton, N. Sandhu, F.I. Chung. Predicting THM formation with artificial neural networks. In North Amer. Wat. and Env. Cong. & Dest. Wat. (1996) 3551-3556.
Google Scholar
[16]
H. Raman, V. Chandramouli. Deriving a general operating policy for reservoirs using neural network. J. of Wat. Res. Plan. and Manag. 122(5) (1996) 342-347.
DOI: 10.1061/(asce)0733-9496(1996)122:5(342)
Google Scholar
[17]
A.R.A. Aziz, K.F.V. Wong. A neural‐network approach to the determination of aquifer parameters. Groundwater 30(2) (1992) 164-166.
Google Scholar
[18]
P.C. Nayak, Y.S. Rao, K.P. Sudheer. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Wat. res. res. 20(1) (2006) 77-90.
DOI: 10.1007/s11269-006-4007-z
Google Scholar
[19]
C.C. Yang, S.O. Prasher, R. Lacroix. Applications of artificial neural networks to simulate water-table depths under subirrigation. Can. Wat. Res. J. 21(1) (1996) 27-44.
DOI: 10.4296/cwrj2101027
Google Scholar
[20]
S.K. Starrett, S.K. Starrett, Y.M. Najjar, J.C. & Hill. Neural networks predict pesticide leaching. In North Amer. Wat. and Env. Cong. & Dest. Wat. (1996) 1693-1698.
Google Scholar
[21]
C. Ray, & K.K. Klindworth. Use of artificial neural networks for agricultural chemical assessment of rural private wells. In North Amer. Wat. and Env. Cong. & Dest. Wat. (1996) 1687-1692.
Google Scholar
[22]
L.L. Rogers, F.U. Dowla, F. U. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Wat. Res. Res. 30(2) (1994) 457-481.
DOI: 10.1029/93wr01494
Google Scholar
[23]
S.V.N. Rao, B.S. Thandaveswara, S.M. Bhallamudi, V. Srinivasulu. Optimal groundwater management in deltaic regions using simulated annealing and neural networks. Wat. Res. Manag. 17(6) (2003) 409-428.
DOI: 10.1023/b:warm.0000004921.74256.a9
Google Scholar
[24]
A. Sentas, L. Karamoutsou, N. Charizopoulos, T. Psilovikos, A. Psilovikos, A. Loukas. The use of stochastic models for short-term prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece. In Mul. Dig. Pub. Inst. Proc. 2(11) (2018) 634.
DOI: 10.3390/proceedings2110634
Google Scholar
[25]
M.J. Alizadeh, M.R. Kavianpour. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Mar. pol. Bul. 98(1-2) (2015) 171-178.
DOI: 10.1016/j.marpolbul.2015.06.052
Google Scholar
[26]
S. Emamgholizadeh, H. Kashi, I. Marofpoor, E. Zalaghi. Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int. J. of Env. Sci. and Tech. 11(3) (2014) 645-656.
DOI: 10.1007/s13762-013-0378-x
Google Scholar
[27]
I. Tsoulos, D. Gavrilis, E. Glavas. Neural network construction and training using grammatical evolution. Neurocomputing. 72(1-3) (2008) 269-277.
DOI: 10.1016/j.neucom.2008.01.017
Google Scholar
[28]
A.K. Soni, S. S. Godara, R. Gade, V, Brenia, R.S. Shekhawat, Kuldeep K. Saxena, R. Prasad. Modelling and thermal analysis for automobile piston using ANSYS. Int. J. on Int. Des. and Man. (2022)
DOI: 10.1007/s12008-022-01042-5
Google Scholar
[29]
S. Hiremath, D. S. Chiniwar, Z. Singh, A. Behera, K.K. Saxena, H. M. Vishwanatha. Modelling and simulation of lightweight hollow pins as a substitution for solid shear pins used for assembly joints in aerospace applications. Int. J. on Int. Des. and Man. (2022)
DOI: 10.1007/s12008-022-01081-y
Google Scholar
[30]
I. Petikas, E. Keramaris, V. Kanakoudis. A novel method for the automatic extraction of quality non-planar river cross-sections from digital elevation models. Water. 12(12) (2020) 3553.
DOI: 10.3390/w12123553
Google Scholar
[31]
S. Nevo. The Technology Behind our Recent Improvements in Flood Forecasting. Blog (2020)
Google Scholar
[32]
Information on: https://www.tensorflow.org/
Google Scholar
[33]
L. Benyahya, A. St-Hilaire, T.B. Quarda, B. Bobée, B. Ahmadi-Nedushan, B. Modeling of water temperatures based on stochastic approaches: case study of the Deschutes River. J. of Env. Eng. and Sci., 6(4) (2007) 437-448.
DOI: 10.1139/s06-067
Google Scholar
[34]
A.G. Barto, R.S. Sutton, C.W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE trans. on syst. man and cyb. 5 (1983) 834-846.
DOI: 10.1109/tsmc.1983.6313077
Google Scholar