Skip to main content
Top
Published in: Water Resources Management 2/2024

30-11-2023

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

Authors: Hui Zuo, Gaowei Yan, Ruochen Lu, Rong Li, Shuyi Xiao, Yusong Pang

Published in: Water Resources Management | Issue 2/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Cosgrove BA, Lohmann D, Mitchell KE, Houser PR, Wood EF, Schaake JC, Robock A, Marshall C, Sheffield J, Duan Q, Luo L, Higgins RW, Pinker RT, Tarpley JD, Meng J (2003) Real-time and retrospective forcing in the north american land data assimilation system (nldas) project. J Geophys Res Atmos 108(D22). https://doi.org/10.1029/2002JD003118 Cosgrove BA, Lohmann D, Mitchell KE, Houser PR, Wood EF, Schaake JC, Robock A, Marshall C, Sheffield J, Duan Q, Luo L, Higgins RW, Pinker RT, Tarpley JD, Meng J (2003) Real-time and retrospective forcing in the north american land data assimilation system (nldas) project. J Geophys Res Atmos 108(D22). https://​doi.​org/​10.​1029/​2002JD003118
go back to reference Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. CoRR abs/1603.06995 Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. CoRR abs/1603.06995
go back to reference He X, Luo J, Li P, Zuo G, Xie J (2020) A hybrid model based on variational mode decomposition and gradient boosting regression tree for monthly runoff forecasting. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) 34(2):865–884. https://doi.org/10.1007/s11269-020-02483-x He X, Luo J, Li P, Zuo G, Xie J (2020) A hybrid model based on variational mode decomposition and gradient boosting regression tree for monthly runoff forecasting. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) 34(2):865–884. https://​doi.​org/​10.​1007/​s11269-020-02483-x
go back to reference Jaeger H (2001) The “echo state’’ approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148(34):13 Jaeger H (2001) The “echo state’’ approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148(34):13
go back to reference Thornton M, Shrestha R, Wei Y, Thornton P, Kao SC, Wilson B (2022) Daymet: Daily surface weather data on a 1-km grid for North America, version 4 r1 Thornton M, Shrestha R, Wei Y, Thornton P, Kao SC, Wilson B (2022) Daymet: Daily surface weather data on a 1-km grid for North America, version 4 r1
go back to reference Yu-tong Z, Xiao-min W, Ting L (2019) Characteristic analysis and prediction of runoff based on chaotic wavelet neural network. In 2019 Chinese Control And Decision Conference (CCDC), pp. 1765–1769 Yu-tong Z, Xiao-min W, Ting L (2019) Characteristic analysis and prediction of runoff based on chaotic wavelet neural network. In 2019 Chinese Control And Decision Conference (CCDC), pp. 1765–1769
Metadata
Title
A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series
Authors
Hui Zuo
Gaowei Yan
Ruochen Lu
Rong Li
Shuyi Xiao
Yusong Pang
Publication date
30-11-2023
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 2/2024
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-023-03681-z

Other articles of this Issue 2/2024

Water Resources Management 2/2024 Go to the issue