000			a
999			_c19138 _d19138
003			OSt
005			20230405130609.0
008			230405b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_914482 _aRawat, S. S.
245			_aRadial basis function artificial neural network (rbfann) model for simulating daily runoff from the himalayan watersheds
250			_aVol.41(1), Jan
260			_aRoorkee _bIndian Water Resources Society _c2021
300			_a41-53p.
520			_aIn this paper, a Radial Basis Function Artificial Neural Network (RBFANN) model was developed based on k-means clustering algorithm to simulate the daily rainfall-runoff process in three Himalayan watersheds i.e., Naula, Chaukhutia, and Ramganga located in Uttarakhand State, India. Different network parameters such as learning rate in the function layer (ALR), learning rate in output layer (ALRG), and the number of iterations were optimized. The outcomes of the RBFANN model was evaluated by using statistical (i.e., root mean square error: RMSE, correlation coefficient: CC, and Nash-Sutcliffe efficiency: NSE) and hydrological (i.e., volumetric error: EV) indicators during calibration, cross-validation, and validation phases. The performance of the RBFANN model improved and stabilized within 500 iterations. The model was very sensitive to learning rate in the function layer (ALR), however, not in the output layer (ALRG). Overall results reveal a promising performance of the RBFANN model in simulating the daily runoff in the study catchments.
650		0	_94621 _aCivil Engineering
700			_920413 _aKasiviswanathan, K. S.
773	0		_x0970-6984 _tJournal of indian water resource society _dRoorkee Indian Institute of Technology Roorkee
856			_uhttps://iwrs.org.in/journal/jan2021/6jan.pdf _yClick here
942			_2ddc _cAR