Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

Yun Bai (), Nejc Bezak, Bo Zeng, Chuan Li, Klaudija Sapač and Jin Zhang
Additional contact information
Yun Bai: Chongqing Technology and Business University
Nejc Bezak: University of Ljubljana
Bo Zeng: Chongqing Technology and Business University
Chuan Li: Chongqing Technology and Business University
Klaudija Sapač: University of Ljubljana
Jin Zhang: Jinan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 4, No 3, 1167-1181

Abstract: Abstract Accurate forecasts of daily runoff are essential for facilitating efficient resource planning and management of a hydrological system. In practice, daily runoff is needed for various practical applications and can be predicted using precipitation and evapotranspiration data. To this end, a long short-term memory (LSTM) under a cascade framework (C-LSTM) approach is proposed for forecasting daily runoff. This C-LSTM model is composed of a 2-level forecasting process. (1) In the first level, an LSTM is established to learn the relationship between the precipitation and evapotranspiration at present and to learn several meteorological variables one day in advance. (2) In the second level, an LSTM is constructed to forecast the daily runoff using the historical and simulated precipitation and evapotranspiration data produced by the first LSTM. Through cascade modeling, the complex features of the numerous targets in the different stages can be sufficiently extracted and learned by multiple models in a single framework. In order to evaluate the performance of the C-LSTM approach, four mesoscale sub-catchments of the Ljubljanica River in Slovenia were investigated. The results indicate that based on the root-mean-square error, the Pearson correlation coefficient, and the Nash-Sutcliffe model efficiency coefficient, the proposed model yields better results than two other tested models, including the normal LSTM and other neural network approaches. Based on the results of this study, we conclude that the LSTM under the cascade architecture is a valuable approach and can be regarded as a promising model for forecasting daily runoff.

Keywords: Long short-term memory; Cascade framework; Meteorological conditions; Precipitation –evapotranspiration pattern; Daily runoff forecast (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-020-02759-2 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:35:y:2021:i:4:d:10.1007_s11269-020-02759-2

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

DOI: 10.1007/s11269-020-02759-2

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 ().