Monthly streamflow forecasting based on improved support vector machine model

Authors:
Jun Guo;Jianzhong Zhou;Hui Qin;Qiang Zou;Qingqing Li
Affiliations:
College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 11
Cited 1

Support-Vector Networks

Machine Learning
Practical method for determining the minimum embedding dimension of a scalar time series

Physica D
Automatic and interactive e-Learning auxiliary material generation utilizing particle swarm optimization

Expert Systems with Applications: An International Journal
Particle swarm optimization for parameter determination and feature selection of support vector machines

Expert Systems with Applications: An International Journal
PSO-based single multiplicative neuron model for time series prediction

Expert Systems with Applications: An International Journal
A Precise Chaotic Particle Swarm Optimization Algorithm based on Improved Tent Map

ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Generalization performance of support vector machines and neural networks in runoff modeling

Expert Systems with Applications: An International Journal
Review: Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

Expert Systems with Applications: An International Journal
A modified particle swarm optimization for production planningproblems in the TFT Array process

Expert Systems with Applications: An International Journal
Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting

Expert Systems with Applications: An International Journal
Nonlinear hydrological time series forecasting based on the relevance vector regression

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

To improve the performance of the support vector machine (SVM) model in predicting monthly streamflow, an improved SVM model with adaptive insensitive factor is proposed in this paper. Meanwhile, considering the influence of noise and the disadvantages of traditional noise eliminating technologies, here the wavelet denoise method is applied to reduce or eliminate the noise in runoff time series. Furthermore, in order to avoid the subjective arbitrariness of artificial judgment, the phase-space reconstruction theory is introduced to determine the structure of the streamflow prediction model. The feasibility of the proposed model is demonstrated through a case study, and the results are compared with the results of artificial neural network (ANN) model and conventional SVM model. The results verify that the improved SVM model can process a complex hydrological data series better, and is of better generalization ability and higher prediction accuracy.