In this paper, the use of Artificial Neural Networks (ANN) has been the subject of flood forecasting in the Oued Isser watershed located in North Western Algeria. The latter covers the largest Tafna’s basin area with the most important wadi in the region.

Two models have been developed; the first one with data at a daily frequency and the second one with instantaneous data, the aim is to see which model represents the most the studied watershed in flood forecasting. Four criteria were used to judge the models performance, the NASH criterion, the coefficient of determination (R2), the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE).

The results obtained show that the two models perform well with the specific features of each and can in this case be used together, improving the effectiveness of flood forecasting.


Flood forecasting, Artificial Neural Networks, Oued Isser Basin, daily data, instantaneous data.

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