Nonlinear hydrological time series forecasting based on the relevance vector regression

Authors:
Fang Liu;Jian-Zhong Zhou;Fang-Peng Qiu;Jun-Jie Yang;Li Liu
Affiliations:
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 2
Cited 1

Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research

Monthly streamflow forecasting based on improved support vector machine model

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

As long leading-time hydrological forecast is a complex non-linear procedure, traditional methods are easy to get slow convergence and low efficiency. The basic relevance vector machine (BRVM) and the developed sequential relevance vector machine (SRVM) are employed to forecast multi-step ahead hydrological time series. The relevance vector machine is a sparse approximate Bayesian kernel method, and it provides full probabilistic forecasting results, which is helpful for hydrological engineering decision. BRVM and SRVM are respectively applied to the annual coming runoff forecast of Three Gorges hydropower station as case study. When compared with auto regression moving average models, BRVM exhibits high model efficiency and provides satisfying forecasting precision. SRVM is potential for its increased freedom and adaptive model selection mechanism. Comparison is also made within direct forecast and iterative one-step ahead forecasting for multi-step ahead forecasting, and the latter shows the ability of highlighting the model performance.