Application of a Hybrid Model Based on Secondary Decomposition and ELM Neural Network in Water Level Prediction-Reference-Cited by-同舟云学术

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Application of a Hybrid Model Based on Secondary Decomposition and ELM Neural Network in Water Level Prediction

Published:2024-04 Issue:2 Volume:29 Page:
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Bai Yulong¹^ORCID,Xing Wenyan²,Ding Lin³,Yu Qinghe²,Song Wei²,Zhu Yajie²

Affiliation:

1. Professor, College of Physics and Electrical Engineering, Northwest Normal Univ., Lanzhou, Gansu 730070, China (corresponding author). ORCID: .

2. Master’s Student, College of Physics and Electrical Engineering, Northwest Normal Univ., Lanzhou, Gansu 730070, China.

3. Ph.D. Student, College of Physics and Electrical Engineering, Northwest Normal Univ., Lanzhou, Gansu 730070, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JHYEFF.HEENG-5946

Reference51 articles.

1. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

2. Optimizing connection weights in neural networks using the whale optimization algorithm

3. Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

4. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

5. Improved complete ensemble EMD: A suitable tool for biomedical signal processing

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