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Water 2017, 9(1), 9;

Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
University of Chinese Academy of Sciences, Beijing 100101, China
Author to whom correspondence should be addressed.
Academic Editor: Marco Franchini
Received: 26 September 2016 / Revised: 14 December 2016 / Accepted: 21 December 2016 / Published: 28 December 2016
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Long-term streamflow forecasting is crucial to reservoir scheduling and water resources management. However, due to the complexity of internally physical mechanisms in streamflow process and the influence of many random factors, long-term streamflow forecasting is a difficult issue. In the article, we mainly investigated the ability of the Relevance Vector Machine (RVM) model and its applicability for long-term streamflow forecasting. We chose the Dahuofang (DHF) Reservoir in Northern China and the Danjiangkou (DJK) Reservoir in Central China as the study sites, and selected the 500 hpa geopotential height in the northern hemisphere and the sea surface temperatures in the North Pacific as the predictor factors of the RVM model and the Support Vector Machine (SVM) model, and then conducted annual streamflow forecasting. Results indicate that forecasting results in the DHF Reservoir is much better than that in the DJK Reservoir when using SVM, because streamflow process in the latter basin has a magnitude bigger than 1000 m3/s. Comparatively, accurate forecasting results in both the two basins can be gotten using the RVM model, with the Nash Sutcliffe efficiency coefficient bigger than 0.7, and they are much better than those gotten from the SVM model. As a result, the RVM model can be an effective approach for long-term streamflow forecasting, and it also has a wide applicability for the streamflow process with a discharge magnitude from dozen to thousand cubic meter per second. View Full-Text
Keywords: long-term streamflow forecasting; relevance vector machine; support vector machine; hydrological process long-term streamflow forecasting; relevance vector machine; support vector machine; hydrological process

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Liu, Y.; Sang, Y.-F.; Li, X.; Hu, J.; Liang, K. Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model. Water 2017, 9, 9.

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