Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model

Yong Liu,Kang Liang,Xinxin Li,Jian Hu,Yan-Fang Sang

doi:10.3390/w9010009

Abstract

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.

Highlights

Conducting streamflow forecasting, especially long-term streamflow forecasting at monthly, annual, inter-annual or even decadal scales, is an important precondition for reservoir scheduling, water resources management, flood control and many other practical water activities [1,2]
The Relevance Vector Machine (RVM) model can be an effective approach for long-term streamflow forecasting, and it has a wide applicability for the streamflow process with a discharge magnitude from dozen to thousand cubic meter per second
A large number of methods have been developed and improved for the streamflow forecasting. They can be generally divided into two types: process-driven methods and data-driven methods [5]. The former are based on mathematical simulation of streamflow process and the internally physical mechanisms that contribute to the hydrological cycle [6]

Summary

Introduction

Conducting streamflow forecasting, especially long-term streamflow forecasting at monthly, annual, inter-annual or even decadal scales, is an important precondition for reservoir scheduling, water resources management, flood control and many other practical water activities [1,2]. It is a difficult task in practice due to the stochastic and nonlinear characteristics of streamflow process at multi-time scales [3,4]. A large number of methods have been developed and improved for the streamflow forecasting They can be generally divided into two types: process-driven methods and data-driven methods [5]. Data-driven methods usually identify and describe the correlation between inputs and outputs, without considering the physical mechanisms of hydrological

Objectives

Results

Conclusion