Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir

Yun Wang,Yanlai Zhou,Shenglian Guo,Hua Chen

doi:10.1007/s00477-013-0772-4

Yun Wang, Yanlai Zhou + Show 2 more

Open Access

https://doi.org/10.1007/s00477-013-0772-4

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Abstract

Due to the complexity of influencing factors and the limitation of existing scientific knowledge, current monthly inflow prediction accuracy is unable to meet the requirements of various water users yet. A flow time series is usually considered as a combination of quasi-periodic signals contaminated by noise, so prediction accuracy can be improved by data preprocess. Singular spectrum analysis (SSA), as an efficient preprocessing method, is used to decompose the original inflow series into filtered series and noises. Current application of SSA only selects filtered series as model input without considering noises. This paper attempts to prove that noise may contain hydrological information and it cannot be ignored, a new method that considerers both filtered and noises series is proposed. Support vector machine (SVM), genetic programming (GP), and seasonal autoregressive (SAR) are chosen as the prediction models. Four criteria are selected to evaluate the prediction model performance: Nash–Sutcliffe efficiency, Water Balance efficiency, relative error of annual average maximum (REmax) monthly flow and relative error of annual average minimum (REmin) monthly flow. The monthly inflow data of Three Gorges Reservoir is analyzed as a case study. Main results are as following: (1) coupling with the SSA, the performance of the SVM and GP models experience a significant increase in predicting the inflow series. However, there is no significant positive change in the performance of SAR (1) models. (2) After considering noises, both modified SSA-SVM and modified SSA-GP models perform better than SSA-SVM and SSA-GP models. Results of this study indicated that the data preprocess method SSA can significantly improve prediction precision of SVM and GP models, and also proved that noises series still contains some information and has an important influence on model performance.

Highlights

Accurate hydrological prediction is an important non-engineering measure to ensure flood control safety and increase water resources use efficiency, and can provide guidance for reservoir planning and management
Main results are as following: (1) coupling with the Singular spectrum analysis (SSA), the performance of the Support vector machine (SVM) and genetic programming (GP) models experience a significant increase in predicting the inflow series
Results of this study indicated that the data preprocess method SSA can significantly improve prediction precision of SVM and GP models, and proved that noises series still contains some information and has an important influence on model performance

Summary

Introduction

Accurate hydrological prediction is an important non-engineering measure to ensure flood control safety and increase water resources use efficiency, and can provide guidance for reservoir planning and management. Runoff is a complicated hydrologic process and has many influencing factors, such as geomorphology, climate, human activity, etc. This makes inflow series become a nonlinear and highly complex non-stationary series. A number of models for dealing with hydrologic time series prediction have been developed, such as classic regressive analysis techniques (Matalas 1967; Salas et al 1982), and more sophisticated methods based on the use of fuzzy logic (Chang and Chen 2001; Nayak et al 2005), artificial neural network (Hsu et al 1995; Aksoy and Dahamsheh 2009), chaos theory (Sivakumar 2009), support vector machine (SVM) (Liong and Sivapragasm 2002), genetic programming (GP) (Whigam and Crapper 2001) and so on.

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