IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v82y2016i2d10.1007_s11069-016-2220-5.html
   My bibliography  Save this article

Comparison of a fuzzy control and the data-driven model for flood forecasting

Author

Listed:
  • Yixiang Sun

    (Hohai University)

  • Deshan Tang

    (Hohai University)

  • Yifei Sun

    (Heilongjiang Provincial Qing Da Water Conservancy and Hydropower Engineering Corporation Limited)

  • Qingfeng Cui

    (Heilongjiang Provincial San Jiang Engineering Construction Administration Bureau)

Abstract

A novel hybrid of adaptive neuro-fuzzy inference system (ANFIS) and two-dimensional Mamdani fuzzy controller was developed for accurately forecasting the water level at the Three Georges Reservoir during the flood season in China. Using statistical approaches, nine input variables were selected based on the upper water levels in the reservoir and the quantity of interval rainfall. Since rainfall is an important input variable in flood forecasting during the flood season, ANFIS was modified to account for the influence of rainfall. Two sub-models were written, ANFIS 1 with rainfall and ANFIS 2 without rainfall, due to the weak cross-correlation function between the interval rainfall and the forecasted water levels. These two sub-models were trained by adjusting the number of the membership functions and the fuzzy rules. The number of membership functions and fuzzy rules was as selected 5 for ANFIS1, because of the relatively better results obtained based on the evaluation criterion in comparison with the other groups. The two-dimensional Mamdani fuzzy controller was regarded as an updating process for ANFIS forecasting, which controlled the error rate between the observed and forecasted amounts to within 0.05 %. The final forecasted results were acquired through error feedback and proved to be very close to the observed results. These results verified that this novel model has accurate predictive capabilities.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:82:y:2016:i:2:d:10.1007_s11069-016-2220-5
    DOI: 10.1007/s11069-016-2220-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2220-5
    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/s11069-016-2220-5?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. Vinit Sehgal & Rajeev Sahay & Chandranath Chatterjee, 2014. "Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1733-1749, April.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," 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. 84(3), pages 2145-2162, December.

    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. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    2. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    3. 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.
    4. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    5. Vinit Sehgal & Mukesh Tiwari & Chandranath Chatterjee, 2014. "Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2793-2811, August.
    6. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," 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. 84(3), pages 2145-2162, December.
    7. Vahid Moosavi & Ali Talebi & Mohammad Reza Hadian, 2017. "Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 43-59, January.
    8. Gaurav Singh & A. R. S. Kumar & R. K. Jaiswal & Surjeet Singh & R. M. Singh, 2022. "Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 12311-12339, October.
    9. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    10. Mandeep Kaur & Pankaj Deep Kaur & Sandeep Kumar Sood, 2021. "Energy efficient IoT-based cloud framework for early flood prediction," 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. 109(3), pages 2053-2076, 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:82:y:2016:i:2:d:10.1007_s11069-016-2220-5. 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.