气候变化领域集成服务门户(CCPortal): Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy

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DOI	10.1016/j.jhydrol.2019.03.101
	Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy
	Wang, Lili; Li, Xin; Ma, Chunfeng; Bai, Yulong
通讯作者	Li, X (通讯作者)
发表日期	2019
ISSN	0022-1694
EISSN	1879-2707
起始页码	733
结束页码	745
卷号	573
英文摘要	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.
关键词	EXTREME LEARNING-MACHINE SINGULAR SPECTRUM ANALYSIS HYBRID MODEL FORECASTING ACCURACY WAVELET ALGORITHM REGION EMD
英文关键词	Streamflow prediction; Data-driven model; Extreme learning machine; Singular spectrum analysis; Empirical mode decomposition
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000474327800058
来源期刊	JOURNAL OF HYDROLOGY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259458
推荐引用方式 GB/T 7714	Wang, Lili,Li, Xin,Ma, Chunfeng,et al. Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy[J]. 中国科学院青藏高原研究所,2019,573.
APA	Wang, Lili,Li, Xin,Ma, Chunfeng,&Bai, Yulong.(2019).Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy.JOURNAL OF HYDROLOGY,573.
MLA	Wang, Lili,et al."Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy".JOURNAL OF HYDROLOGY 573(2019).