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

Hui Zuo (), Gaowei Yan (), Ruochen Lu (), Rong Li (), Shuyi Xiao () and Yusong Pang ()
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 2, No 5, 503 pages

Abstract: 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.

Keywords: Runoff prediction; Chaos theory; Reservoir computing; Multi-task learning; Convolutional neural network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-023-03681-z

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