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Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes

Author

Listed:
  • Jiun-Huei Jang

    (National Cheng Kung University)

  • Kun-Fang Lee

    (National Cheng Kung University)

  • Jin-Cheng Fu

    (National Science and Technology Center for Disaster Reduction)

Abstract

Over the last 50 years, physics-based numerical and machine learning (ML) models have been the two major tools for river-stage forecasting—the former are more accurate but time-consuming in model construction and computation, whereas the latter are faster but lack of physical backup. To extract the advantages and avoid the shortages of the two models in isolation, four hybrid ML models were developed in this study for river-stage forecasting using multiple additive regression trees (MART) driven under different orders of Runge–Kutta (RK) numerical schemes. The models are trained and tested using 13 typhoon events between 2015 and 2019 in the Keelung River Basin, Taiwan. Compared with the original MART without RK schemes, the hybrid models greatly reduce the errors by 29% and 53% in the predictions of mean and peak river stages, respectively. Meanwhile, the patterns of the river stages predicted by the hybrid models fluctuate less and are more accurate when lead time increases. Requiring fewer training times than the original MART models, and less computation time than pure numerical models, the hybrid models are more economical and efficient in the application of real-time river-stage forecasting. The positive results show that the combination of different ML models and numerical schemes has great potential for improving hydrological forecasting that requires further studies in the future.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:3:d:10.1007_s11269-022-03077-5
    DOI: 10.1007/s11269-022-03077-5
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    References listed on IDEAS

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    1. Xinyu Wan & Qingyan Yang & Peng Jiang & Ping’an Zhong, 2019. "A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 4027-4050, September.
    2. Jhih-Huang Wang & Gwo-Fong Lin & Ming-Jui Chang & I-Hang Huang & Yu-Ren Chen, 2019. "Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3759-3780, September.
    3. 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.
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