Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm

Shengli Liao (), Huan Wang (), Benxi Liu (), Xiangyu Ma (), Binbin Zhou () and Huaying Su ()
Additional contact information
Shengli Liao: Dalian University of Technology
Huan Wang: Dalian University of Technology
Benxi Liu: Dalian University of Technology
Xiangyu Ma: Dalian University of Technology
Binbin Zhou: Yunnan Electric Power Dispatch Control Center of Yunnan Power Grid
Huaying Su: Power Dispatching Control Center of Guizhou Power Grid

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 4, No 5, 1539-1555

Abstract: Abstract Accurate long-term runoff forecasting is crucial for managing and allocating water resources. Due to the complexity and variability of natural runoff, the most difficult problems currently faced by long-term runoff forecasting are the difficulty of model construction, poor prediction accuracy, and time intensive forecasting processes. Therefore, this study proposes a hybrid long-term runoff forecasting framework that uses the antecedent inflow and specific meteorological factors as the inputs, is modeled by ensemble empirical mode decomposition (EEMD) coupled with an artificial neural network (ANN), and computed by a parallel algorithm. First, the framework can transform monthly inflow and meteorological series into stationary signals via EEMD to more comprehensively explore the relationships of the input factors through the ANN. Second, the selected meteorological factors that are closely related to inflow formation can be filtered out by the single correlation coefficient method, which contributes to reducing coupling between input factors, and increases the accuracy of the prediction models. Finally, a multicore parallel algorithm that is easily accessed everywhere and that fully utilizes multiple calculation resources while flexibly contending with various optimization requirements will improve forecasting efficiency. The Xiaowan Hydropower Station (XW) is selected as the study area, and the final results of the study show that (1) the addition of targeted meteorological factors does indeed greatly enhance the performance of the prediction models; (2) the five criteria for evaluating the prediction accuracy show that the EEMD-ANN model is far superior to the prediction performance from the ordinary ANN model when run under the same input conditions; and (3) the optimization time of the 32-core model can be reduced by as much as 25 times, which significantly saves time during the forecast process.

Keywords: Long-term runoff forecast; Parallel algorithm; Ensemble empirical mode decomposition; ANN; Filtered meteorological factors (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-023-03442-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-023-03442-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-023-03442-y

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().