Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study-Reference-Cited by-同舟云学术

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Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study

Published:2022-12-02 Issue:1 Volume:37 Page:557-581
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Yang Hao,Li Weide^ORCID

Funder

National Natural Sciences Foundation of China

Natural Science Foundation of Gansu Province of China

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-022-03389-6.pdf

Reference28 articles.

1. Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675

2. Gauch M, Kratzert F, Klotz D, Nearing G, Lin J, Hochreiter S (2021) Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network. Hydrol Earth Syst Sci 25(4):2045–2062. https://doi.org/10.5194/hess-25-2045-2021

3. He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054

4. Jahandideh-Tehrani M, Jenkins G, Helfer F (2021) A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia. Optim Eng 22(1):29–50. https://doi.org/10.1007/s11081-020-09538-3

5. Kadkhodazadeh M, Farzin S (2022) Introducing a novel hybrid machine learning model and developing its performance in estimating water quality parameters. Water Resour Manag 36(10):3901–3927. https://doi.org/10.1007/s11269-022-03238-6

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