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An artificial neural network model for generating hydrograph from hydro-meteorological parameters
 

Summary: An artificial neural network model for generating hydrograph
from hydro-meteorological parameters
Sajjad Ahmada,1
, Slobodan P. Simonovicb,
*
a
Department of Civil, Architectural and Environmental Engineering, University of Miami, Coral Gables, FL 33146-0630, USA
b
Department of Civil and Environmental Engineering and Institute for Catastrophic Loss Reduction,
University of Western Ontario, London, Ont., Canada N6A 5B9
Received 22 September 2003; revised 7 March 2005; accepted 31 March 2005
Abstract
Conceptual models are considered to be the best choice for describing the runoff process in a watershed. However, enormous
requirements for topographic, hydrologic and meteorological data and extensive time commitment for calibration of conceptual
models (especially for distributed models) are often prohibitive factors in their practical applications. Artificial neural networks
(ANN) can be an efficient way of modeling the runoff process in situations where explicit knowledge of the internal hydrologic
processes is not available. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear
relationships between input and output data sets. This paper presents the use of ANN for predicting the peak flow, timing and
shape of runoff hydrograph, based on causal meteorological parameters. Antecedent precipitation index, melt index, winter
precipitation, spring precipitation, and timing are the five input parameters used to develop runoff hydrograph for the Red River

  

Source: Ahmad, Sajjad - Department of Civil and Environmental Engineering, University of Nevada at Las Vegas

 

Collections: Engineering