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.
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Liu, F., Zhou, JZ., Qiu, FP., Yang, JJ., Liu, L. (2006). Nonlinear Hydrological Time Series Forecasting Based on the Relevance Vector Regression. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_97
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DOI: https://doi.org/10.1007/11893257_97
Publisher Name: Springer, Berlin, Heidelberg
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