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A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series

Author

Listed:
  • Hui Zuo

    (Taiyuan University of Technology)

  • Gaowei Yan

    (Taiyuan University of Technology)

  • Ruochen Lu

    (Taiyuan University of Technology)

  • Rong Li

    (Taiyuan University of Technology)

  • Shuyi Xiao

    (Taiyuan University of Technology)

  • Yusong Pang

    (Delft University of Technology)

Abstract

Accurately predicting runoff is crucial for managing water resources, preventing and mitigating floods, scheduling hydropower plant operations, and protecting the environment. The hydrological dynamic composite system that forms runoff is complex and random, and seemingly random behavior may be caused by nonlinear variables in a simple deterministic system, which poses a challenge to runoff prediction. In this paper, we construct parallel and multi-timescale reservoirs from a chaotic theory perspective to simulate the stochasticity of chaotic systems. We propose a multi-task-based "Decomposition-Integration-Prediction" (Multi-SDIPC) model for runoff prediction. To validate our research results, we use the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset and compare our proposed model with 10 baseline models. The results show that our model has an average NSE metric of 0.83 and exhibits higher accuracy, better generalization, and greater stability than the other models in multi-step forecasting. Based on our findings, we recommend wider application of the Multi-SDIPC model in different regions of the world for medium or long-term runoff prediction.

Suggested Citation

  • Hui Zuo & Gaowei Yan & Ruochen Lu & Rong Li & Shuyi Xiao & Yusong Pang, 2024. "A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(2), pages 481-503, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:2:d:10.1007_s11269-023-03681-z
    DOI: 10.1007/s11269-023-03681-z
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    References listed on IDEAS

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    1. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    2. Zhou, Zhong-bing & Dong, Xiu-cheng, 2012. "Analysis about the seasonality of China's crude oil import based on X-12-ARIMA," Energy, Elsevier, vol. 42(1), pages 281-288.
    3. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
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    Cited by:

    1. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Chun-Tian Cheng & Dong-mei Xu & Wen-Tao Zhuang, 2024. "Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(9), pages 3135-3152, July.

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