IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i15d10.1007_s11269-020-02708-z.html
   My bibliography  Save this article

Predictability of Monthly Streamflow Time Series and its Relationship with Basin Characteristics: an Empirical Study Based on the MOPEX Basins

Author

Listed:
  • Ran-Ran He

    (Hohai University
    Bengbu University)

  • Yuanfang Chen

    (Hohai University)

  • Qin Huang

    (Hohai University)

  • Zheng-Wei Pan

    (Bengbu University)

  • Yong Liu

    (Nanjing Hydraulic Research Institute)

Abstract

Machine learning (ML) models have been applied to monthly streamflow forecasting in recent decades. In this study, forecasting skills of eight ML models are evaluated based on the Model Parameter Estimation Experiment (MOPEX) dataset. We consider two skill scores, i.e., the Nash–Sutcliffe efficiency (NSE) and the adjusted NSE (ANSE), and the latter is the skill score based on the interannual mean monthly value (MMV) as the reference (benchmark) model. Furthermore, NSE of the MMV model (NSEmmv) is used as a measure of the seasonality of monthly streamflow, as it is the ratio of variance explained by the MMV process. An important result is that forecasting skills of ML models for monthly streamflow are largely controlled by NSEmmv. Moreover, based on comparisons of different ML models, we have found that the selection of models is not a dominating factor impacting the final skill. Three key factors influencing NSE, i.e., NSEmmv, the base flow index (BFI) and the aridity index (AI), are explored in this paper. Specifically, NSEmmv impacts NSE directly and is the predominant factor; BFI influences the memory of the monthly streamflow and therefore influences NSE. The relationship between AI and NSE is much complex and indirect. Firstly, basins with higher AI tend to have lower NSEmmv, and this will lead to lower NSE; secondly, basins with higher AI tend to have lower BFI, which will also lead to lower NSE; thirdly, for a given BFI level, basins with higher AI tend to have higher memory and higher NSE. For ANSE, basins with AI between 1 and 2 show higher ANSE, which corresponds to higher autocorrelation coefficients.

Suggested Citation

  • Ran-Ran He & Yuanfang Chen & Qin Huang & Zheng-Wei Pan & Yong Liu, 2020. "Predictability of Monthly Streamflow Time Series and its Relationship with Basin Characteristics: an Empirical Study Based on the MOPEX Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4991-5007, December.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02708-z
    DOI: 10.1007/s11269-020-02708-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-020-02708-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-020-02708-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gianluca Bontempi & Souhaib Ben Taieb & Yann-Aël Le Borgne, 2013. "Machine learning strategies for time series forecasting," ULB Institutional Repository 2013/167761, ULB -- Universite Libre de Bruxelles.
    2. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    3. Ali Ahani & Mojtaba Shourian & Peiman Rahimi Rad, 2018. "Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 383-399, January.
    4. Shivshanker Patel & Parthasarathy Ramachandran, 2015. "A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 589-602, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Twumasi, Clement & Twumasi, Juliet, 2022. "Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1258-1277.
    3. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
    4. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    7. Robyn Horan & Pawan S. Wable & Veena Srinivasan & Helen E. Baron & Virginie J. D. Keller & Kaushal K. Garg & Nathan Rickards & Mike Simpson & Helen A. Houghton-Carr & H. Gwyn Rees, 2021. "Modelling Small-Scale Storage Interventions in Semi-Arid India at the Basin Scale," Sustainability, MDPI, vol. 13(11), pages 1-28, May.
    8. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    9. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    10. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    11. Anastasios Petropoulos & Vassilis Siakoulis & Konstantinos P. Panousis & Loukas Papadoulas & Sotirios Chatzis, 2023. "Macroeconomic forecasting and sovereign risk assessment using deep learning techniques," Papers 2301.09856, arXiv.org.
    12. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    13. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
    14. Koffi, Siméon, 2022. "Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm [Inflation Forecasting in Côte D'Ivoire: A Comparative Analysis of the Arima, Holt-Winters, and," MPRA Paper 113961, University Library of Munich, Germany.
    15. repec:ers:journl:v:xxiv:y:2021:i:special2:p:513-522 is not listed on IDEAS
    16. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
    17. Marinoiu Cristian, 2018. "AVERAGE MONTHLY RAINFALL FORECAST IN ROMANIA BY USING k-NEAREST NEIGHBORS REGRESSION," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 5-12, August.
    18. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    19. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    20. Lingqi Li & Kai Wu & Enhui Jiang & Huijuan Yin & Yuanjian Wang & Shimin Tian & Suzhen Dang, 2021. "Evaluating Runoff-Sediment Relationship Variations Using Generalized Additive Models That Incorporate Reservoir Indices for Check Dams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3845-3860, September.
    21. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02708-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.