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

Journal article (2023)

Authors

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
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Hui Zuo, Gaowei Yan, Ruochen Lu, Rong Li, Shuyi Xiao, Y. Pang
DOI
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https://doi.org/10.1007/s11269-023-03681-z
Convolutional neural network Multi-task learning Chaos theory Reservoir computing Runoff prediction
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

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.