Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting

Maryam Rahimzad (), Alireza Moghaddam Nia (), Hosam Zolfonoon (), Jaber Soltani (), Ali Danandeh Mehr () and Hyun-Han Kwon ()
Additional contact information
Maryam Rahimzad: University of Isfahan
Alireza Moghaddam Nia: University of Tehran
Hosam Zolfonoon: INESC Coimbra, University of Coimbra
Jaber Soltani: Aburaihan Campus, University of Tehran
Ali Danandeh Mehr: Antalya Bilim University
Hyun-Han Kwon: Sejong University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 12, No 15, 4167-4187

Abstract: Abstract Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation ( $$R$$ R ), Nash-Sutcliff coefficient of efficiency ( $$E$$ E ), Nash-Sutcliff for High flow ( $${E}_{H}$$ E H ), Nash-Sutcliff for Low flow ( $${E}_{L}$$ E L ), normalized root mean square error ( $$NRMSE$$ NRMSE ), relative error in estimating maximum flow ( $$REmax$$ REmax ), threshold statistics ( $$TS$$ TS ), and average absolute relative error ( $$AARE$$ AARE ) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of $$NRMSE$$ NRMSE and the highest values of $${E}_{H}$$ E H , $${E}_{L}$$ E L , and $$R$$ R under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.

Keywords: Streamflow forecasting; Data-driven modeling; LSTM; Deep learning; Machine learning algorithms (search for similar items in EconPapers)
Date: 2021
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/s11269-021-02937-w 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:12:d:10.1007_s11269-021-02937-w

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

DOI: 10.1007/s11269-021-02937-w

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 ().