A new hybrid artificial neural networks for rainfall–runoff process modeling

This paper proposes a hybrid intelligent model for runoff prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and levenberg–marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation, input variables selection and data clustering for improving the accuracy of the model. The capability of the proposed method is tested by applying it to predict runoff at the Aghchai watershed. The results show that this approach is able to predict runoff more accurately than Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models.