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Weibiao Qiao
Researcher at North China University of Water Conservancy and Electric Power
Publications - 20
Citations - 987
Weibiao Qiao is an academic researcher from North China University of Water Conservancy and Electric Power. The author has contributed to research in topics: Pipeline transport & Wavelet transform. The author has an hindex of 10, co-authored 15 publications receiving 563 citations. Previous affiliations of Weibiao Qiao include Liaoning University of Petroleum and Chemical Technology & Sinopec.
Papers
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Journal ArticleDOI
The Forecasting of PM2.5 Using a Hybrid Model Based on Wavelet Transform and an Improved Deep Learning Algorithm
TL;DR: A novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers is proposed and the conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.
Journal ArticleDOI
A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer
Weibiao Qiao,Weibiao Qiao,Hongfang Lu,Hongfang Lu,Guofeng Zhou,Mohammadamin Azimi,Quan Yang,Wencai Tian +7 more
TL;DR: A novel hybrid algorithm is proposed, which combines lion swarm optimizer and genetic algorithm to optimize the traditional least squares support vector machine model, which is utilized to forecast carbon dioxide emissions in various countries from 2018 to 2025.
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A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine
TL;DR: A hybrid prediction model that integrates an improved whale swarm algorithm (IWOA) and relevance vector machine (RVM) and empirical mode decomposition (EMD), approximate entropy (ApEn), and C-C method are introduced to aid the calculation.
Journal ArticleDOI
Solving Large-Scale Function Optimization Problem by Using a New Metaheuristic Algorithm Based on Quantum Dolphin Swarm Algorithm
Weibiao Qiao,Zhe Yang +1 more
TL;DR: The ability of QDSA to obtain global optimal solution is obviously improved compared with DSA, and the performance ofQDSA is superior to other algorithms considered for comparison.
Journal ArticleDOI
Modified Dolphin Swarm Algorithm Based on Chaotic Maps for Solving High-Dimensional Function Optimization Problems
Weibiao Qiao,Zhe Yang +1 more
TL;DR: Chaos mapping is introduced into DSA, and chaotic dolphin swarm algorithm (CDSA) is successfully proposed, and the performance of CDSA outperform that of the state-of-the-art algorithms considered to be compared.