IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i10d10.1007_s11269-024-03834-8.html
   My bibliography  Save this article

Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow

Author

Listed:
  • Priyanka Sharma

    (National Institute of Hydrology)

  • Farshad Fathian

    (Vali-e-Asr University of Rafsanjan)

  • Deepesh Machiwal

    (ICAR-Central Arid Zone Research Institute)

  • S. R. Bhakar

    (CTAE, MPUAT)

  • Survey D. Sharma

    (National Institute of Hydrology)

Abstract

Streamflow estimation plays an important role in water resources planning and management, and minimizes the impact of floods and droughts. Therefore, this study aims to estimate monthly streamflows in a semi-arid region of India by employing multiple data-driven models, which include standalone time-series models such as Thomas-Fiering (T-F), autoregressive moving average (ARMA), and the logistic smooth threshold autoregressive (LSTAR) models, artificial intelligence (AI) technique such as artificial neural network (ANN), and an advanced novel hybrid model such as LSTAR coupled with generalized autoregressive conditional heteroscedasticity (GARCH), i.e., LSTAR-GARCH model. Furthermore, capability of five models including standalone models (T-F, ARMA), a cascade feed-forward back propagation neural network (CFBPNN), LSTAR, and the hybrid LSTAR-GARCH model, was comparatively evaluated in estimating 40-year (June 1975 to May 2015) monthly streamflow of Jakham reservoir located in Rajasthan state of India. The models were calibrated using first 75% of the dataset, and their predictions were evaluated using four performance evaluation criteria: correlation coefficient (R), root mean square error (RMSE), modified Nash-Sutcliffe efficiency (MNSE), and modified index of agreement (MIA). The developed models were validated using remaining 25% dataset (120 months), and their performances were compared by plotting hydrographs and scatter plots between the observed and estimated values. The results suggested that the T-F model was satisfactory for generating synthetic monthly streamflow while the ARMA model accurately estimated the streamflows except at extremes. Among the stand-alone models, CFBPNN-4 model outperformed the other three ones with a higher MNSE value of 0.53, and a lower RMSE value of 30.78 million m3. The proposed hybrid LSTAR-GARCH model, which is based on a novel framework, outperformed the standalone ones in terms of R, MNSE, MIA, and RMSE (0.89, 0.78, 0.58, and 25.92 million m3, respectively).

Suggested Citation

  • Priyanka Sharma & Farshad Fathian & Deepesh Machiwal & S. R. Bhakar & Survey D. Sharma, 2024. "Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3685-3705, August.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:10:d:10.1007_s11269-024-03834-8
    DOI: 10.1007/s11269-024-03834-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03834-8
    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/s11269-024-03834-8?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. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, October.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Hamed Nozari & Fateme Tavakoli & Mohamad Mohamadi, 2019. "Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1913-1926, April.
    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. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    3. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    4. Alessandro Cardinali, 2012. "An Out-of-sample Analysis of Mean-Variance Portfolios with Orthogonal GARCH Factors," International Econometric Review (IER), Econometric Research Association, vol. 4(1), pages 1-16, April.
    5. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    6. Lim, G.C. & McNelis, Paul D., 2008. "Computational Macroeconomics for the Open Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262123061, April.
    7. Alberto Humala & Gabriel Rodriguez, 2013. "Some stylized facts of return in the foreign exchange and stock markets in Peru," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 30(2), pages 139-158, May.
    8. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    9. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    10. Babikir, Ali & Gupta, Rangan & Mwabutwa, Chance & Owusu-Sekyere, Emmanuel, 2012. "Structural breaks and GARCH models of stock return volatility: The case of South Africa," Economic Modelling, Elsevier, vol. 29(6), pages 2435-2443.
    11. Marçal, Emerson Fernandes & Pereira, Pedro L. Valls, 2008. "Testing the Hypothesis of Contagion Using Multivariate Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(2), November.
    12. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
    13. Ventosa-Santaulària, Daniel & Mendoza V., Alfonso, 2005. "Non Linear Moving-Average Conditional Heteroskedasticity," MPRA Paper 58769, University Library of Munich, Germany.
    14. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    15. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    16. Calmès, Christian & Théoret, Raymond, 2014. "Bank systemic risk and macroeconomic shocks: Canadian and U.S. evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 388-402.
    17. Sascha Mergner & Jan Bulla, 2008. "Time-varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques," The European Journal of Finance, Taylor & Francis Journals, vol. 14(8), pages 771-802.
    18. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    19. Barry A. Goss & S. Gulay Avsar, 2013. "Simultaneity, Forecasting and Profits in London Copper Futures," Australian Economic Papers, Wiley Blackwell, vol. 52(2), pages 79-96, June.
    20. Papantonis Ioannis & Rompolis Leonidas S. & Tzavalis Elias & Agapitos Orestis, 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.

    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:38:y:2024:i:10:d:10.1007_s11269-024-03834-8. 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.