Abstract
Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results.
Highlights
Assessing and estimating water available in a basin over long periods is an important precondition for efficient reservoir scheduling and water supply
The results of the experiments of this study have demonstrated that empirical wavelet transform (EWT) is an effective way to filter the noises of annual streamflow series and improving prediction accuracy, and the multi-verse optimizer (MVO) algorithm exhibits better performance compared with the Levenberg–Marquart (LM) and particle swarm optimization (PSO) algorithms in the training of artificial neural network (ANN) models for annual streamflow forecasting
The computations related to all of the models including back propagation neural network (BPNN), PSO-ANN, MVO-ANN, EWT-BPNN, EWT-PSO-ANN and EWT-MVO-ANN are implemented in the MATLAB environment in a computer with Intel core i5, 2.6-GHz CPU and 4 GB of RAM
Summary
Assessing and estimating water available in a basin over long periods (from months to years) is an important precondition for efficient reservoir scheduling and water supply. For this reason, constructing a forecasting model for long-term streamflow time series is essential [1]. Methods for long-term streamflow forecasting can be mainly grouped into two categories: physically-based models and data-driven models. Physical-based models usually consider multi-factors including rainfall, runoff, evaporation, soil moisture, infiltration, snow cover and melt, etc., while data-driven models usually take advantage of the implicit information contained in the historical streamflow time series to forecast the future streamflow [2]. The autoregressive moving average (ARMA) approach, since first being proposed by Box and Jenkins [4] and popularized by Carlson et al [5], has been
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
