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.
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Data Availability
The data that support the findings of this study are openly available in Hydrology and Earth System Sciences at https://doi.org/10.5194/hess-21-5293-2017, 2017.
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Funding
This work was supported by the National Natural Science Foundation of China (61973226, 62003233), the Shanxi Provincial Department of Water Resources 2023 Water Technology Research and Promotion Subsidy Projects(2023GM17), the Shanxi Province Major Special Program of Science and Technology “Unveiling and Commanding“ Project(202201090301013), the Natural Science Foundation of Shanxi Province(202203021222101).
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Hui Zuo: conceptualization, methodology, software, visualization, formal analysis, writing-original draft preparation. Gaowei Yan: conceptualization, methodology, writing-review and editing, supervision, funding acquisition. Ruochen Lu: supervision, software, writing-review & editing. Rong Li: conceptualization, methodology, writing-review & editing. Shuyi Xiao: conceptualization, methodology writing-review & editing. Yusong Pang: conceptualization, supervision, Writing-review & editing.
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Zuo, H., Yan, G., Lu, R. et al. A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series. Water Resour Manage 38, 481–503 (2024). https://doi.org/10.1007/s11269-023-03681-z
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DOI: https://doi.org/10.1007/s11269-023-03681-z