Climatology
Hosein Rahmati; Samad Gholizadeh; Hosein Ansari
Abstract
Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye ...
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Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye basin with an area of 112 km2 and average annual rainfall of 306.72mm, two different models namely WetSpa and artificial neural network (ANN) were used. To run of the WetSpa model two categories of information, including raster maps and metrological data and for ANN model only meteorological data were used. The 5 years data were used to simulation runoff of Bara-Ariye basin. The statistical parameters such as correlation coefficient (R2), the square of the standard error of the mean (RMSE) and mean absolute error (MAE) were used for comparison results of two models. The results indicated that the WetSpa model with R2 and RMSE equal to 0.920 and 0.346 m3/s and also ANN model with R2 and RMSE equal to 0.959 and 0.310 m3/s have the ability to simulate runoff of Bara Ariye River. Also using neural network model reduced the error estimation of watershed runoff 11.6% compared with the WetSpa model.
