FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2908904086> ?p ?o ?g. }

• W2908904086 endingPage "184" @default.
• W2908904086 startingPage "167" @default.
• W2908904086 abstract "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." @default.
• W2908904086 created "2019-01-25" @default.
• W2908904086 creator A5015768486 @default.
• W2908904086 creator A5037282810 @default.
• W2908904086 creator A5043058614 @default.
• W2908904086 creator A5049021556 @default.
• W2908904086 creator A5065141057 @default.
• W2908904086 creator A5090899965 @default.
• W2908904086 date "2019-03-01" @default.
• W2908904086 modified "2023-10-15" @default.
• W2908904086 title "Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems" @default.
• W2908904086 cites W1132038451 @default.
• W2908904086 cites W1197692462 @default.
• W2908904086 cites W1715353133 @default.
• W2908904086 cites W1740585449 @default.
• W2908904086 cites W1854912902 @default.
• W2908904086 cites W1967955389 @default.
• W2908904086 cites W1970978817 @default.
• W2908904086 cites W1984915657 @default.
• W2908904086 cites W1985479415 @default.
• W2908904086 cites W1987345571 @default.
• W2908904086 cites W1995277704 @default.
• W2908904086 cites W2000982976 @default.
• W2908904086 cites W2002404570 @default.
• W2908904086 cites W2006119367 @default.
• W2908904086 cites W2009465763 @default.
• W2908904086 cites W2013619180 @default.
• W2908904086 cites W2020564882 @default.
• W2908904086 cites W2033185628 @default.
• W2908904086 cites W2033904036 @default.
• W2908904086 cites W2035811055 @default.
• W2908904086 cites W2037460094 @default.
• W2908904086 cites W2039049978 @default.
• W2908904086 cites W2041490648 @default.
• W2908904086 cites W2041678850 @default.
• W2908904086 cites W2044781495 @default.
• W2908904086 cites W2046156160 @default.
• W2908904086 cites W2046794274 @default.
• W2908904086 cites W2050051610 @default.
• W2908904086 cites W2052851219 @default.
• W2908904086 cites W2055385291 @default.
• W2908904086 cites W2058998445 @default.
• W2908904086 cites W2062087947 @default.
• W2908904086 cites W2062174566 @default.
• W2908904086 cites W2067183857 @default.
• W2908904086 cites W2067578568 @default.
• W2908904086 cites W2068059354 @default.
• W2908904086 cites W2071272611 @default.
• W2908904086 cites W2075604548 @default.
• W2908904086 cites W2079541639 @default.
• W2908904086 cites W2086189650 @default.
• W2908904086 cites W2087007385 @default.
• W2908904086 cites W2092213200 @default.
• W2908904086 cites W2094618865 @default.
• W2908904086 cites W2102148524 @default.
• W2908904086 cites W2106665847 @default.
• W2908904086 cites W2111072639 @default.
• W2908904086 cites W2120390927 @default.
• W2908904086 cites W2121745948 @default.
• W2908904086 cites W2121971770 @default.
• W2908904086 cites W2132984323 @default.
• W2908904086 cites W2141695047 @default.
• W2908904086 cites W2147746661 @default.
• W2908904086 cites W2153697770 @default.
• W2908904086 cites W2154250668 @default.
• W2908904086 cites W2155431069 @default.
• W2908904086 cites W2165313910 @default.
• W2908904086 cites W2165761444 @default.
• W2908904086 cites W2168087114 @default.
• W2908904086 cites W2173733059 @default.
• W2908904086 cites W2255781275 @default.
• W2908904086 cites W2262639697 @default.
• W2908904086 cites W2274744025 @default.
• W2908904086 cites W2282605543 @default.
• W2908904086 cites W2290873233 @default.
• W2908904086 cites W2298132732 @default.
• W2908904086 cites W2299617127 @default.
• W2908904086 cites W2300217830 @default.
• W2908904086 cites W2321536237 @default.
• W2908904086 cites W2369565839 @default.
• W2908904086 cites W2398936495 @default.
• W2908904086 cites W2426187208 @default.
• W2908904086 cites W2467690446 @default.
• W2908904086 cites W2491189118 @default.
• W2908904086 cites W2493918211 @default.
• W2908904086 cites W2518675717 @default.
• W2908904086 cites W2522984776 @default.
• W2908904086 cites W2540357196 @default.
• W2908904086 cites W2550265600 @default.
• W2908904086 cites W2564782988 @default.
• W2908904086 cites W2566512888 @default.
• W2908904086 cites W2570501755 @default.
• W2908904086 cites W2571217044 @default.
• W2908904086 cites W2581811121 @default.
• W2908904086 cites W2581822685 @default.
• W2908904086 cites W2586634262 @default.
• W2908904086 cites W2592903613 @default.
• W2908904086 cites W2595609330 @default.

• next