Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data

Rainfall-runoff modeling is necessary for many hydrological studies, such as estimating peak discharges and designing hydraulic structures. The intensity and frequency of extreme climatic events necessitate the use of advanced approaches that incorporate different climatic and landscape parameters for rainfall-runoff modeling. The majority of small basins around the world lack hydrometric data. This study applied an artificial neural network (ANN) to simulate the rainfall-runoff process using data from field sampling plots in conjunction with rainfall and hydrometric data. For this purpose, similarly sized field plots were established among different land uses to determine the amounts of initial loss and infiltration during rainfall occurrences at the Talar basin in the north of Iran. The modeling process was carried out using a multi-layer perceptron network where the network inputs were rainfall time series, initial loss, soil antecedent moisture condition (A.M.C), and the time to peak of the basin, and the output was runoff time series. The data from rain gauge and hydrometric stations and field plots were collected for three consecutive months. The threefold exercises of training ( R -sqr = 0.96, MSE = 0.005), cross-validation ( R -sqr = 0.95, MSE = 0.006), and test ( R -sqr = 0.81, MSE = 0.05) have yielded favorable results. The modeling results also indicated the significance of the cumulative rainfall data and initial loss in the modeling process. Results show that runoff time series and flood hydrograph can be simulated using the optimal inputs and an appropriate neural network structure for the basins without active hydrometric stations.