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

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 in Manitoba, Canada. A feed-forward artificial neural network is trained by using back-percolation algorithm. Peak flow, time of peak, width of hydrograph at 75 and 50% of peak, base flow, and timing of rising and falling sides of hydrograph are the output parameters obtained from the neural network model to describe a runoff hydrograph. The ANN generated results are evaluated using statistical parameters: percentage error and correlation. For six flood events for which forecasts are made the average absolute error in peak flow and time of peak is 6% and 4 days, respectively. Correlation between observed and simulated values of peak flow and time of peak is 0.99 and 0.88, respectively.