Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework-Reference-Cited by-同舟云学术

Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework

Published:2024-10 Issue:5 Volume:29 Page:
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Jin Aohan¹,Wang Quanrong²,Zhou Renjie³^ORCID,Shi Wenguang¹,Qiao Xiangyu¹

Affiliation:

1. School of Environmental Studies, China Univ. of Geosciences, Wuhan, Hubei 430074, PR China.

2. Professor, School of Environmental Studies, China Univ. of Geosciences, Wuhan, Hubei 430074, PR China; Professor, State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, Ministry of Ecology and Environment, 388 Lumo Rd., Wuhan 430074, PR China (corresponding author).

3. Professor, Dept. of Environmental and Geosciences, Sam Houston State Univ., Huntsville, TX 77340. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JHYEFF.HEENG-6254

Reference51 articles.

1. Daily streamflow prediction using optimally pruned extreme learning machine

2. Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

3. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction

4. Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions

5. A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches