IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i13d10.1007_s11269-019-02357-x.html
   My bibliography  Save this article

River Stage Forecasting Using Multiple Additive Regression Trees

Author

Listed:
  • Jin-Cheng Fu

    (National Science and Technology Center for Disaster Reduction)

  • Hsiao-Yun Huang

    (Fu Jen Catholic University)

  • Jiun-Huei Jang

    (National Cheng Kung University)

  • Pei-Hsun Huang

    (National Cheng Kung University)

Abstract

Accurate real-time forecasts of river stages can serve as a reference for flood evacuation to minimize losses and casualties. Machine learning has been widely used for river stage forecasting because of its simple modeling and quick computation. However, many machine learning models have drawbacks such as excessive learning time, difficult evaluation of input variables, and lack of explanatory capacity, which limit their performance as practical tools. To overcome these drawbacks, this study employs multiple additive regression trees (MART) for river stage forecasting. Three MART models are proposed, namely the original MART model, the real-time MART model, and the naïve MART model, with different considerations of model training and error correction. Model training and testing were conducted based on the rainfall and river stage data for 16 typhoon events between 2005 and 2009 in the Bazhang River Basin in Taiwan. In the training process, variables are automatically selected by the MART models which reasonably describes the mechanism of flood transportation. The testing results show that all three models can reasonably forecast the river stages with a three-hour lead-time. Compared with the original MART, the real-time MART performs better in describing overall river stage variations, whereas the naïve MART is more accurate in the prediction of peak river stages. The proposed MART models are efficient and accurate and can thus serve as practical tools for flash flood early warning.

Suggested Citation

  • Jin-Cheng Fu & Hsiao-Yun Huang & Jiun-Huei Jang & Pei-Hsun Huang, 2019. "River Stage Forecasting Using Multiple Additive Regression Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4491-4507, October.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:13:d:10.1007_s11269-019-02357-x
    DOI: 10.1007/s11269-019-02357-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02357-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/s11269-019-02357-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. Youngmin Seo & Sungwon Kim & Ozgur Kisi & Vijay P. Singh & Kamban Parasuraman, 2016. "River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 4011-4035, September.
    2. Jaydip Makwana & Mukesh Tiwari, 2014. "Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4857-4873, October.
    3. S. Aggarwal & Arun Goel & Vijay Singh, 2012. "Stage and Discharge Forecasting by SVM and ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3705-3724, October.
    4. M. A. Ghorbani & R. Khatibi & V. Karimi & Zaher Mundher Yaseen & M. Zounemat-Kermani, 2018. "Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4201-4215, October.
    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. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
    2. Siva R Venna & Satya Katragadda & Vijay Raghavan & Raju Gottumukkala, 2021. "River Stage Forecasting using Enhanced Partial Correlation Graph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4111-4126, September.
    3. Jiun-Huei Jang & Petr Vohnicky & Yen-Lien Kuo, 2021. "Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2215-2230, May.
    4. Jiun-Huei Jang & Kun-Fang Lee & Jin-Cheng Fu, 2022. "Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1123-1140, February.
    5. Ana C. Cebrián & Ricardo Salillas, 2021. "Forecasting High-Frequency River Level Series Using Double Switching Regression with ARMA Errors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 299-313, January.

    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. Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
    2. Mustafa Najat Asaad & Şule Eryürük & Kağan Eryürük, 2022. "Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Xue-hua Zhao & Xu Chen, 2015. "Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2913-2926, June.
    4. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    5. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    6. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    7. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    8. Vijendra Kumar & Naresh Kedam & Kul Vaibhav Sharma & Khaled Mohamed Khedher & Ayed Eid Alluqmani, 2023. "A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    9. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    10. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
    11. Jinlin Li & Lanhui Zhang, 2021. "Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    12. Subbarayan Saravanan & Nagireddy Masthan Reddy & Quoc Bao Pham & Abdullah Alodah & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi, 2023. "Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset," Sustainability, MDPI, vol. 15(16), pages 1-26, August.
    13. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    15. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    16. Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
    17. Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.
    18. Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
    19. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    20. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.

    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:33:y:2019:i:13:d:10.1007_s11269-019-02357-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.