Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling techniq...Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.展开更多
Complete and incomplete fusion cross sections have been measured for the 20Ne + 51V system to investigate the influence of projectile break-up at or above the barrier energies. The excitation function of the individua...Complete and incomplete fusion cross sections have been measured for the 20Ne + 51V system to investigate the influence of projectile break-up at or above the barrier energies. The excitation function of the individual reaction channel measured using off-line γ-ray spectroscopy has been compared with those estimated theoretically using the statistical model code PACE4. A significant fraction of incomplete fusion has been found in the production of residues involving α-particle in the exit channel. In order to understand the energy dependence of incomplete fusion reactions, incomplete fusion fraction has been deduced, which has also been compared with the earlier data available for 12C and 16O projectiles to look for the projectile structure dependence.展开更多
文摘Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.
文摘Complete and incomplete fusion cross sections have been measured for the 20Ne + 51V system to investigate the influence of projectile break-up at or above the barrier energies. The excitation function of the individual reaction channel measured using off-line γ-ray spectroscopy has been compared with those estimated theoretically using the statistical model code PACE4. A significant fraction of incomplete fusion has been found in the production of residues involving α-particle in the exit channel. In order to understand the energy dependence of incomplete fusion reactions, incomplete fusion fraction has been deduced, which has also been compared with the earlier data available for 12C and 16O projectiles to look for the projectile structure dependence.
