Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions

This paper discusses the use of artificial neural network (ANN) models for predicting daily flows from Khosrow Shirin watershed located in the northwest part of Fars province in Iran. A Multi-Layer Perceptron (MLP) neural network was developed using five input vectors leading to five ANN models: MLP1, MLP2, MLP3, MLP4, and MLP5. Two activation functions were used and they were logistic sigmoid and tangent sigmoid. The MLP_Levenberg–Marquardt (LM) algorithm was used for the training of ANN models. A 5-year data record, selected randomly, was used for ANN training and testing. The predicted outflow showed that the tangent sigmoid activation function performed better than did the logistic sigmoid activation function. The values of R <Superscript>2</Superscript> and RMSE for MLP4 with the tangent sigmoid activation function for the validation period were equal to 0.89 and 1.7 m<Superscript>3</Superscript>/s, respectively. Appropriate input vectors for MLPs were determined by correlation analysis. It was found that antecedent precipitation and discharge with 1 day time lag as an input vector best predicted daily flows. Also, comparison of MLPs showed that an increase in input data was not always useful. Copyright Springer Science+Business Media B.V. 2010