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Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow

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Abstract

Annual streamflow prediction is of great significance to the sustainable utilization of water resources, and predicting it accurately is challenging due to changes in streamflow have strong nonlinearity and uncertainty. To improve the prediction accuracy of annual streamflow, this study proposes a new hybrid prediction model based on extracting information from high-frequency components of streamflow. In the proposed model, the original streamflow data is decomposed by ensemble empirical mode decomposition (EEMD) into several intrinsic mode functions (IMFs) with different frequencies. Then, the dominant component and residual component are identified from the high-frequency components IMF1 and IMF2 using singular spectrum analysis (SSA), and the residual components are accumulated as a new component. Finally, all the components, including the new component that is not noise, are modelled by support vector machine (SVM), and the SVM is optimized by grey wolf optimizer (GWO). To analyse and verify the proposed model, the annual streamflow data are collected from the Liyuan River and Taolai River in the Heihe River Basin, and six models, autoregressive integrated moving average (ARIMA), cross validation (CV)-SVM, GWO-SVM, EEMD-ARIMA, EEMD-GWO-SVM and modified EEMD-GWO-SVM are considered as comparison models. The results indicate that the prediction performance of the proposed model is obviously better than that of other reference models, and extracting valuable information from high-frequency components can effectively improve annual streamflow prediction. Thus, the high-frequency components contained in the original streamflow series have an important impact on obtaining accurate streamflow prediction, and the proposed model makes full use of the high-frequency components and provides a reliable method for streamflow prediction.

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Availability of Data and Materials

The data used in this study are available from the corresponding author on request.

Abbreviations

EEMD:

Ensemble empirical mode decomposition

IMF:

Intrinsic mode functions

SSA:

Singular spectrum analysis

SVM:

Support vector machine

GWO:

Grey wolf optimizer

ARIMA:

Autoregressive integrated moving average

CV:

Cross validation

ANN:

Artificial neural network

VC:

Vapnik-Chervonenkis

RF:

Random forest

EMD:

Empirical mode decomposition

VMD:

Variational mode decomposition

GBRT:

Gradient boosting regression

PSO:

Particle swarm optimization

DE:

Differential evolution

ABC:

Artificial bee colony

RA:

Residual-component accumulation

SVD:

Singular value decomposition

RBF:

Radial basis function

PACF:

Partial autocorrelation function

MAE:

Mean absolute error

RMSE:

Root mean square error

MAPE:

Mean absolute percentage error

R:

Coefficient of correlation

NSEC:

Nash-Sutcliffe efficiency coefficient

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Funding

This work was supported by the Scientific Research Program of the Higher Education Institutions of Gansu Province (2020A-016), and the Foundation of Northwest Normal University of China (NWNU-LKQN2019-18, NWNU-LKQN2020-21).

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Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, China

    Lili Wang & Manhong Fan

  2. Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou, 730070, China

    Lili Wang & Manhong Fan

  3. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

    Yanlong Guo

Authors
  1. Lili Wang
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  2. Yanlong Guo
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  3. Manhong Fan
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Contributions

Lili Wang: Conceptualization, Methodology, Software, Writing – original draft, review and editing. Yanlong Guo: Formal analysis, Visualization, Writing – review and editing. Manhong Fan: Investigation, Validation, Writing – review and editing.

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Correspondence to Lili Wang.

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Wang, L., Guo, Y. & Fan, M. Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow. Water Resour Manage 36, 4535–4555 (2022). https://doi.org/10.1007/s11269-022-03262-6

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  • DOI: https://doi.org/10.1007/s11269-022-03262-6

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