Artificial Neural Network (ANN) a data driven technique is utilised for modelling runoff.
The proportionality hypothesis, originating from the curve number method at the event scale, is extended for modeling runoff in the water-limited and energy-limited seasons, respectively.
The drainage morphometric analyses are prerequisite for selñection of water recharge site, watershed modeling, runoff modeling, watershed delineation, groundwater prospect mapping and geotechnical investigation (Magesh et al. 2011; Thomas et al. 2012).
After a rigorous calibration with rain-gauge data, the hydrological response of the basin to this flooding is accurately simulated by the Hydrologic Engineering Center's Hydrological Modeling System runoff model.
The objective of this study was to incorporate the time-varying PCA concept into hydrological modeling for runoff simulation.
Runoff from ungauged catchments is estimated using a simple rainfall runoff model and runoff coefficients.
The model predicted runoff of 0.31 m3/s, compared to a measured value of 0.46 m3/s.
Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input.
The Soil and Water Assessment Tool (SWAT) was used to model daily runoff and sediment data from this basin collected from 1975 to 1995.
