Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Peibing Song,Zhenxue Nong,Chao Wang,Hao Wang,Weifeng Liu,Xiaohui Lei,Jiahui Sun,Lingzhong Kong

doi:10.3390/w12082086

Abstract

Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

Highlights

Hydrological forecasting, especially medium and long-term runoff forecasting, is an indispensable part of water resources management and water conservancy projects’ operation [1,2,3]
The annual runoff forecasting model presented in this paper consists of four parts: extraction of key forecasting factors based on the principal component analysis (PCA) method; comparison of the key forecasting factors based on the principal component analysis (PCA) method; comparison of the predicted runoff time series of four forecasting models, including the multiple linear regression (MLR)
Based on annual runoff time series from 1964 to 2015 of the Dongbei Hydrological Station, annual rainfall time series from 1963 to 2015, and 130 monitoring indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes) from 1963 to 2014, the forecasting factors are screened according to the principle of selecting principal components whose cumulative contribution rate is greater than 85%

Summary

Introduction

Hydrological forecasting, especially medium and long-term runoff forecasting, is an indispensable part of water resources management and water conservancy projects’ operation [1,2,3]. Medium and long-term runoff forecasting, with a forecast period of. In order to improve the accuracy and reliability of a runoff forecasting model, scholars at home and abroad have carried out massive application studies, in terms of selecting forecasting models and screening forecasting factors. As far as forecasting models are concerned, cause analysis methods, mathematical statistics methods, and artificial intelligence methods proposed for improving the runoff prediction accuracy have received tremendous attention over the past decades [8,9]. Cause analysis methods pay attention to the physical formation process of hydrological phenomena, which comprehensively consider the influence of atmospheric circulation, meteorological factors, and the underlying surface physical environment on runoff variation. It is demonstrated that key hydrometeorological events, such as sunspot, EI Nino, ocean currents oscillation, and plateau snow, are closely related to runoff [10,11]

Objectives

Methods

Results

Conclusion