IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i9d10.1007_s11069-024-06528-x.html
   My bibliography  Save this article

A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran

Author

Listed:
  • Marzieh Khajehali

    (Isfahan University of Technology)

  • Hamid R. Safavi

    (Isfahan University of Technology)

  • Mohammad Reza Nikoo

    (Sultan Qaboos University)

  • Mahmood Fooladi

    (Isfahan University of Technology)

Abstract

This study proposes a novel fusion framework for flood forecasting based on machine-learning, statistical, and geostatistical models for daily multiple-step-ahead and near-future under climate change scenarios. An efficient machine-learning model with three remote-sensing precipitation products, including ERA5, CHIRPS, and PERSIANN-CDR, was applied to gap-fill data. Four individual machine-learning models, including Random Forest, Multiple-Layer Perceptron, Support Vector Machine, and Extreme Learning Machine, were developed twelve days ahead of streamflow modeling. Then, three fusion models, including Random Forest, Bayesian Model Averaging, and Bayesian Maximum Entropy, were applied to combine the outputs of individual machine-learning models. The proposed framework was also implemented to downscale the precipitation variables of three general climate models (GCMs) under SSP5-8.5 and SSP1-2.6 scenarios. The application of this approach is investigated on the Kan River, Iran. The results indicated that individual models illustrated weak performance, especially in far-step-ahead flood forecasting, so it is necessary to utilize a fusion technique to improve the results. The RF model indicated high efficiency in the fusion step compared to other fusion-based models. This technique also demonstrated effective proficiency in downscaling daily precipitation data of GCMs. Finally, the flood forecasting model was developed based on the fusion framework in the near future (2020–2040) by using precipitation data from two scenarios. We conclude that flood events based on SSP5-8.5 and SSP1-2.6 will increase in the future in our case study. Also, the frequency evaluation shows that floods under SSP1-2.6 will occur about 10% more than SSP5-8.5 in the Kan River basin from 2020 to 2040.

Suggested Citation

  • Marzieh Khajehali & Hamid R. Safavi & Mohammad Reza Nikoo & Mahmood Fooladi, 2024. "A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8483-8504, July.
  • Handle: RePEc:spr:nathaz:v:120:y:2024:i:9:d:10.1007_s11069-024-06528-x
    DOI: 10.1007/s11069-024-06528-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06528-x
    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/s11069-024-06528-x?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. Shima Kamali & Keyvan Asghari, 2023. "The Effect of Meteorological and Hydrological Drought on Groundwater Storage Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2925-2943, June.
    2. Zaw Latt & Hartmut Wittenberg, 2014. "Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2109-2128, June.
    3. Ruhhee Tabbussum & Abdul Qayoom Dar, 2021. "Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 519-566, August.
    4. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    5. Francis Yongwa Dtissibe & Ado Adamou Abba Ari & Chafiq Titouna & Ousmane Thiare & Abdelhak Mourad Gueroui, 2020. "Flood forecasting based on an artificial neural network scheme," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1211-1237, November.
    6. Phuoc Nguyen & Lloyd Chua & Lam Son, 2014. "Flood forecasting in large rivers with data-driven models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 767-784, March.
    7. Nurünnisa Usul & Burak Turan, 2006. "Flood forecasting and analysis within the Ulus Basin, Turkey, using geographic information systems," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 39(2), pages 213-229, October.
    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. Roland Brown & Yingling Fan & Kirti Das & Julian Wolfson, 2021. "Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data," Biometrics, The International Biometric Society, vol. 77(2), pages 401-412, June.
    2. Hyemin Han, 2024. "Bayesian Model Averaging and Regularized Regression as Methods for Data-Driven Model Exploration, with Practical Considerations," Stats, MDPI, vol. 7(3), pages 1-13, July.
    3. Emanuel Kopp, 2018. "Determinants of U.S. Business Investment," IMF Working Papers 2018/139, International Monetary Fund.
    4. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    5. Liao, Jun & Zou, Guohua, 2020. "Corrected Mallows criterion for model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    6. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    7. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
    8. Mihai MUTASCU & Nicolae-Bogdan IANC & ALBERT LESSOUA, 2021. "Public debt and inequality in Sub-Saharan Africa: the case of EMCCA and WAEMU countries," LEO Working Papers / DR LEO 2909, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    9. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.
    10. Chengbiao Fu & Heigang Xiong & Anhong Tian, 2018. "Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content," Mathematics, MDPI, vol. 6(12), pages 1-11, December.
    11. Huihang Liu & Xinyu Zhang, 2023. "Frequentist model averaging for undirected Gaussian graphical models," Biometrics, The International Biometric Society, vol. 79(3), pages 2050-2062, September.
    12. Francisco Alonso & Sergio A. Useche & Eliseo Valle & Cristina Esteban & Javier Gene-Morales, 2021. "Could Road Safety Education (RSE) Help Parents Protect Children? Examining Their Driving Crashes with Children on Board," IJERPH, MDPI, vol. 18(7), pages 1-13, March.
    13. Grover,Arti Goswami & Lall,Somik V. & Timmis,Jonathan David, 2021. "Agglomeration Economies in Developing Countries : A Meta-Analysis," Policy Research Working Paper Series 9730, The World Bank.
    14. Yixiang Sun & Deshan Tang & Yifei Sun & Qingfeng Cui, 2016. "Comparison of a fuzzy control and the data-driven model for flood forecasting," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 827-844, June.
    15. Yin-Wong Cheung & Wenhao Wang, 2020. "A Jackknife Model Averaging Analysis of RMB Misalignment Estimates," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-45, June.
    16. Marwah Soliman & Vyacheslav Lyubchich & Yulia R. Gel, 2020. "Ensemble forecasting of the Zika space‐time spread with topological data analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    17. Enrique Labrada & Luis Huesca, "undated". "Data management in household income and expenditure surveys: Working with extended families using Stata," Mexican Stata Conference 2023 19, Stata Users Group.
    18. Proloy Deb & Prankanu Debnath & Anjelo Francis Denis & Ong Tshering Lepcha, 2019. "Variability of soil physicochemical properties at different agroecological zones of Himalayan region: Sikkim, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2321-2339, October.
    19. Yaroslav Vyklyuk & Milan Radovanović & Boško Milovanović & Taras Leko & Milan Milenković & Zoran Milošević & Ana Milanović Pešić & Dejana Jakovljević, 2017. "Hurricane genesis modelling based on the relationship between solar activity and hurricanes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1043-1062, January.
    20. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.

    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:nathaz:v:120:y:2024:i:9:d:10.1007_s11069-024-06528-x. 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.