Author(s): Lateef Ahmad Dar
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Volume. 6 - Issue. 04 , April - 2017
Hydrologic engineering design and management purposes require information about runoff from a hydrologic catchment. In order to predict this information, the transformation of rainfall on a catchment to runoff from it must be modelled. One approach to this modelling issue is to use empirical Rainfall-Runoff (R-R) models. Artificial neural networks (ANNs) are among the most sophisticated empirical models available and have proven to be especially good in modelling complex systems. Their ability to extract relations between inputs and outputs of a process, without the physics being explicitly provided to them, theoretically suits the problem of relating rainfall to runoff well, since it is a highly nonlinear and complex problem. The goal of this investigation was to develop rainfall-runoff models for the river Jhelum catchment that are capable of accurately modelling the relationships between rainfall and runoff in a catchment. It is for this reason that ANN and MLR techniques were tested as R-R models on a data set from the upper Jhelum catchment in Jammu and Kashmir, India. For modeling the rainfall-runoff process in river Jhelum ,the input i.e. the precipitation was determined by taking the data from three rainguage stations viz. Srinagar, Pahalgam and Qazigund for years 2001-2013. The runoff data was taken for padshahibagh guaging station that lies in Srinagar, summer capital of Jammu and Kashmir for the years 2001-2013. From the predicative analysis , it was found that the flow at the Padshahibagh on any given day is dependent on previous three day flows .The number of previous day rainfall inputs influencing the discharge was determined by the trial and error method, the number of previous day rainfall inputs were increased from one to five. The comparison was based on various statistical parameters like root mean square error (RMSE) and R2.
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