Compare the accuracy of artificial neural network and regression models to estimate flood (Case Study: Urban area of Joghatay)

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Abstract. In this study, regression and neural network techniques is used to estimate the flood peak discharge moment in Joghatay urban basin with the use of physiographic and climatic stations surrounding the place. In order to assess the stations, Climatology and around the basin of the hydrometer, 8 stations that had at least 37 years of daily data selection were chosen and the following data were used for the input of the model: data area, the slope of the basin, average height, channel length, Gravylyus index, annual rainfall and also flood peak discharge was used as the output of the model. First the peak instantaneous flow rate was estimated by regression and then 63% of inputs were used for training neural network models, and 37% of the remaining data were used for testing the models. Finally, in order to compare the results and assess the efficiency of the method mentioned in the discharge peak moment, we used the correlation and root mean square error. The results showed that the technique of artificial neural network is superior to the regression method to estimate the flood peak discharge point. Based on these results, the problem of short period of peak discharge data in stations related to maximum flow rate can be solved by using artificial neural network, and predicting the watershed flood of the area.


Flood peak discharge, artificial neural network, regression model, the coefficient of correlation, root mean square error minimum, Joghatay

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Department of Natural Resources of Khorasan Razavi

Department of water Resources of Khorasan Razavi

Meteorological department in Khorasan Razavi

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