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

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

Zuo, Hui (Taiyuan University of Technology)
Yan, Gaowei (Taiyuan University of Technology)
Lu, Ruochen (Taiyuan University of Technology)
Li, Rong (Taiyuan University of Technology)
Xiao, Shuyi (Taiyuan University of Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)

2023

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.

Chaos theory
Convolutional neural network
Multi-task learning
Reservoir computing
Runoff prediction

http://resolver.tudelft.nl/uuid:06b29c3e-6ea6-4636-866f-a8bb294d6127

https://doi.org/10.1007/s11269-023-03681-z

2024-05-23

0920-4741

Water Resources Management, 38 (2024) (2), 481-503

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Hui Zuo, Gaowei Yan, Ruochen Lu, Rong Li, Shuyi Xiao, Y. Pang