Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems
文献类型：期刊论文
| 作者 | Wen, Xiaohu1,2; Feng, Qi1,2; Deo, Ravinesh C.3,4; Wu, Min1,2; Yin, Zhenliang1,2; Yang, Linshan1,2; Singh, Vijay P.5,6 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2019-03-01 |
| 卷号 | 570页码:167-184 |
| 关键词 | Expert system Runoff Integrated model Complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) Variational mode decomposition (VMD) Extreme learning machine (ELM) |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2018.12.060 |
| 通讯作者 | Wen, Xiaohu(xhwen@lzb.ac.cn) ; Deo, Ravinesh C.(ravinesh.deo@usq.edu.au) |
| 英文摘要 | Expert systems adopted in real-time multi-scale runoff prediction are useful decision-making tools for hydrologists but the stochastic nature of any hydrological variable can pose significant challenges in attaining a reliable predictive model. This paper advocates a data-driven approach used to design two-phase hybrid model (i.e., CVEE-ELM). The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) coupled with the variational mode decomposition (VMD) algorithms for better frequency resolution of the input datasets and the extreme learning machine (ELM) algorithm as the objective predictive model. In the first stage of the presented model design, notable frequencies in the predictor-target data series are uncovered, utilizing the CEEMDAN algorithm where the model's inputs are decomposed into their respective Intrinsic Mode Functions (IMFs) and the Residual (Res) series. The second stage entails a VMD approach, used to decompose the yet-unresolved high frequencies (i.e., IMF1 ) into their variational modes, further discerning and establishing the data attributes to be incorporated into the ELM model to simulate the respective IMFs, Res and VM data series, aggregated as an integrative tool for multiscale runoff prediction. In the model evaluative phase, the hybrid CVEE-ELM is cross-validated with a single-phase hybrid ELM and an autoregressive integrated moving average (ARIMA) model to benchmark its accuracy for predicting 1-, 3- and 6-month ahead runoff in Yingluoxia watershed, Northwestern China. Two-phase hybrid model exhibits superior accuracy at all lead times, to accord with high degree of correlations between the observed and the forecasted runoff, a relatively large Nash-Sutcliffe and the Legate-McCabe Index. Taylor diagrams depict the two-phase hybrid CVEE-ELM model generated forecasts located close to a reference (i.e., a perfect) model, with a lower root-mean square centered difference, and a correspondingly large correlation for all forecast horizons, although the model's accuracy for shorter lead times (1-month), as expected, are better than the 3- and 6-month horizon. The study shows that the two-phase hybrid CVEE-ELM model where an integration of two frequency resolution algorithms are made, is a preferred data-driven tool that can be explored for real-life decision-system design, particularly for hydrological forecasting problems that have significantly stochastic data features, and thus, will require reliable forecasts to be generated at multi-step horizons. |
| 收录类别 | SCI |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR MACHINE ; IMPROVING FORECASTING ACCURACY ; GLOBAL SOLAR-RADIATION ; HYBRID MODEL ; RIVER FLOW ; INTELLIGENCE MODELS ; DATA ASSIMILATION ; WAVELET ; RAINFALL |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000460709400015 |
| 出版者 | ELSEVIER SCIENCE BV |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2555543 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Wen, Xiaohu; Deo, Ravinesh C. |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Ecohydrol Inland River Basin, Lanzhou, Gansu, Peoples R China 2.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Gansu, Peoples R China 3.Univ Southern Queensland, Sch Agr Computat & Environm Sci, Ctr Sustainable Agr Syst, Springfield, Qld 4300, Australia 4.Univ Southern Queensland, Ctr Appl Climate Sci, Springfield, Qld 4300, Australia 5.Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA 6.Texas A&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843 USA |
| 推荐引用方式 GB/T 7714 | Wen, Xiaohu,Feng, Qi,Deo, Ravinesh C.,et al. Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems[J]. JOURNAL OF HYDROLOGY,2019,570:167-184. |
| APA | Wen, Xiaohu.,Feng, Qi.,Deo, Ravinesh C..,Wu, Min.,Yin, Zhenliang.,...&Singh, Vijay P..(2019).Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems.JOURNAL OF HYDROLOGY,570,167-184. |
| MLA | Wen, Xiaohu,et al."Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems".JOURNAL OF HYDROLOGY 570(2019):167-184. |
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来源：寒区旱区环境与工程研究所
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