IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i9d10.1007_s11269-022-03197-y.html
   My bibliography  Save this article

Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan

Author

Listed:
  • You-Da Jhong

    (Feng Chia University)

  • Hsin-Ping Lin

    (Feng Chia University)

  • Chang-Shian Chen

    (Feng Chia University)

  • Bing-Chen Jhong

    (National Taiwan University of Science and Technology)

Abstract

Accurate hourly real-time flood forecasting is necessary for early flood warning systems, especially during typhoon periods. Artificial intelligence methods have been increasingly used for real-time flood forecasting. This study developed a real-time flood forecasting model by using back-propagation networks (BPNs) with a self-organizing map (SOM) to create ensemble forecasts. Random weights and biases were set for the BPNs to learn the characteristics of a catchment system. An unsupervised SOM network with a classification function was then used to cluster representative BPN weights and biases; clusters of BPNs with high accuracy were selected to act as experts for the ensemble models to forecast flow rates. The model was applied to flood events in the Wu River Basin of Taiwan. Most observed values were within the forecasting intervals of the BPN clusters in the calibration and validation phases, indicating that the models had acceptable accuracy. For the large flood events of typhoons Saola in the calibration phase and Soulik in the validation phase, the mean average error of the ensemble mean model for the cluster A was 143.1 and 327.4 m3/s, respectively; these values were lower than those for the best individual model within the cluster (194.3 and 917.9 m3/s). The ensemble model thus outperformed the individual models and can accurately forecast flood values and intervals. Therefore, the model can be used to accurately forecast floods.

Suggested Citation

  • You-Da Jhong & Hsin-Ping Lin & Chang-Shian Chen & Bing-Chen Jhong, 2022. "Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3221-3245, July.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03197-y
    DOI: 10.1007/s11269-022-03197-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03197-y
    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/s11269-022-03197-y?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. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    2. Muhamad Khoiru Zaki & Keigo Noda & Kengo Ito & Komariah Komariah & Sumani Sumani & Masateru Senge, 2020. "Adaptation to Extreme Hydrological Events by Javanese Society through Local Knowledge," Sustainability, MDPI, vol. 12(24), pages 1-11, December.
    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. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    2. Suwarno & Anang Widhi Nirwansyah & Sutomo & Ismail Demirdag & Esti Sarjanti & Dhi Bramasta, 2022. "The Existence of Indigenous Knowledge and Local Landslide Mitigation: A Case Study of Banyumas People in Gununglurah Village, Central Java, Indonesia," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    3. Vanessa Sari & Nilza Maria Reis Castro & Olavo Correa Pedrollo, 2017. "Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4909-4923, December.
    4. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    5. Natanael Karjanto, 2022. "Revisiting Indigenous Wisdom of Javanese Pranata mangsa . Comment on Zaki et al. Adaptation to Extreme Hydrological Events by Javanese Society through Local Knowledge. Sustainability 2020, 12 , 10373," Sustainability, MDPI, vol. 14(15), pages 1-5, August.
    6. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
    7. Bing-Chen Jhong & Hsi-Ting Fang & Cheng-Chia Huang, 2021. "Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2387-2408, June.
    8. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
    9. Xing-Yun Zou & Xin-Yu Peng & Xin-Xin Zhao & Chun-Ping Chang, 2023. "The impact of extreme weather events on water quality: international evidence," 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. 115(1), pages 1-21, January.
    10. Hunggul Yudono Setio Hadi Nugroho & Yonky Indrajaya & Satria Astana & Murniati & Sri Suharti & Tyas Mutiara Basuki & Tri Wira Yuwati & Pamungkas Buana Putra & Budi Hadi Narendra & Luthfy Abdulah & Tit, 2023. "A Chronicle of Indonesia’s Forest Management: A Long Step towards Environmental Sustainability and Community Welfare," Land, MDPI, vol. 12(6), pages 1-62, June.
    11. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    12. Lubna Jamal Chachan, 2022. "Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey," Technium, Technium Science, vol. 4(1), pages 239-249.
    13. Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.

    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:36:y:2022:i:9:d:10.1007_s11269-022-03197-y. 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.