Abstract
This paper introduces three artificial neural network (ANN) architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE) through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT), were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.
Highlights
Streamflow forecasting has been one of the key issues in hydrology in recent decades.Enhancing streamflow forecasting accuracy is of great significance to various aspects of hydrological system such as water allocation, flood control, and disaster relief.In recent decades, numerous methods and hydrological models have been studied to obtain accurate streamflow predictions
Results of this study indicated that ensemble empirical mode decomposition (EEMD) could enhance forecasting accuracy of medium and long-term runoff time series
The performance evaluation indices of the 12 models developed in this study, including radial basis function (RBF), Extreme Learning Machine (ELM), Elman, simple averaging ensemble (SAE), weighted averaging ensemble (WAE), and generalized regression neural network (GRNN)-based ensemble (GNE) model, with and without the empirical wavelet transform (EWT) algorithm, in the training and validation periods are shown in Tables 2 and 3, respectively
Summary
Streamflow forecasting has been one of the key issues in hydrology in recent decades. Numerous methods and hydrological models have been studied to obtain accurate streamflow predictions. These methods can be grouped into two categories: conceptual models and empirical models [1]. Empirical models are data-driven models which are built using historical information contained in the hydrological time series as opposed to the physical processes of a certain catchment [4,5,6]. The various empirical models involved in hydrological forecasting predominantly include time series models, machine learning methods, and hybrid methods
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