The Application of the Artificial Neural Network Ensemble Model for Simulating Streamflow

Abstract

This study applies the ensemble method with artificial neural network (ANN) for simulating daily discharge. The study area is the Bocheong-cheon watershed, located in the central part of South Korea. The ANN outputs were generated by randomly sampling the initial weights, the nodes of hidden layer, and the training data. The correlation and principal component analysis are used for identifying the input vectors for ANN model. The impact affecting on the performance and the uncertainty of ANN ensemble model is investigated by considering the length of training data, the type of activation functions, the number of hidden nodes and training methods. The results indicate that the performance and uncertainty of the ANN ensemble model are affected by various elements related to ANN model development. The ensemble method applied in this case study provides satisfactory results for the stream flow modelling and is useful for assessing the uncertainty of ANN model. However, the computational efficiency of the ensemble method is not effective and might be problematic for an application that needs a rapid simulation.