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Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy
Wang, LL (Wang, Lili)1,2,3; Li, X (Li, Xin)4,5; Ma, CF (Ma, Chunfeng)1; Bai, YL (Bai, Yulong)2
Source PublicationJOURNAL OF HYDROLOGY
2019
Volume573Issue:0Pages:733-745
DOI10.1016/j.jhydrol.2019.03.101
Abstract

Streamflow forecasting has great significance in water resource management, particularly for reservoir operation. However, accurately predicting streamflow is challenging due to the non-stationary characteristics of hydrologic processes and the effects of noise. To improve monthly streamflow forecasting, this study proposes a data-driven model based on a double-processing strategy, which combines singular spectrum analysis (SSA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extreme learning machine (ELM) approaches. In the proposed double-processing model, called SSA-ICEEMDAN-ELM, the original streamflow series are first processed via SSA for denoising; then, the processed series are reprocessed via ICEEMDAN to decompose them into relatively stationary sub-series; finally, these sub-series are modelled using ELM. The performance of the proposed model is tested for one-month-ahead prediction using streamflow data from the Caojiahu and Shibalipu reservoirs in the Gulang River Basin. In addition, the proposed double-processing model is compared with four single-processing models, namely, empirical mode decomposition (EMD)-ELM, ensemble EMD (EEMD)-ELM, ICEEMDAN-ELM and SSA-ELM, and two single models without any processing, namely, autoregressive integrated moving average (ARIMA) and ELM. The results show that: (a) the four single-processing models have higher prediction accuracy than the single models, and the performance of the SSA-ELM model is the best of these single-processing models, implying that noise in hydrological series cannot be ignored; (b) the proposed SSA-ICEEMDAN-ELM model is superior to the single-processing models and single models, demonstrating that the double-processing approach can further improve streamflow prediction accuracy. Thus, the proposed model, which is a promising method that is expected to benefit reservoir management, can better reduce the influence of noise and capture the dynamic characteristics of hydrological series.

Subject AreaGeology
WOS IDWOS:000474327800058
Language英语
Indexed BySCI
KeywordExtreme Learning-machine Singular Spectrum Analysis Hybrid Model Forecasting Accuracy Wavelet Algorithm Region Emd
WOS Research AreaEngineering ; Geology ; Water Resources
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
Cooperation Status国内
ISSN0022-1694
Department三极观测与大数据研究中心
URL查看原文
PublisherELSEVIER SCIENCE BV
SubtypeArticle
Citation statistics
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/9279
Collection图书馆
Corresponding AuthorLi, X (Li, Xin)
Affiliation1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China;
2.Northwest Normal Univ, Coll Phys & Elect Engn, Lanzhou 730070, Gansu, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
4.Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China;
5.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.
Recommended Citation
GB/T 7714
Wang, LL ,Li, X ,Ma, CF ,et al. Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy[J]. JOURNAL OF HYDROLOGY,2019,573(0):733-745.
APA Wang, LL ,Li, X ,Ma, CF ,&Bai, YL .(2019).Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy.JOURNAL OF HYDROLOGY,573(0),733-745.
MLA Wang, LL ,et al."Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy".JOURNAL OF HYDROLOGY 573.0(2019):733-745.
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