CEEMD-MR-hybrid model based on sample entropy and random forest for SO2 prediction
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
CEEMD-MR-hybrid model based on sample entropy and random forest for SO2 prediction
Authors
Keywords
Ensemble model, Hybrid forecasting, Mode refactor, Air pollution
Journal
Atmospheric Pollution Research
Volume 13, Issue 3, Pages 101358
Publisher
Elsevier BV
Online
2022-02-17
DOI
10.1016/j.apr.2022.101358
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- PM2.5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition
- (2021) Guoyan Huang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque
- (2021) Gang Shi et al. KNOWLEDGE-BASED SYSTEMS
- Air pollution concentration forecasting based on wavelet transform and combined weighting forecasting model
- (2021) Bingchun Liu et al. Atmospheric Pollution Research
- An advanced weighted system based on swarm intelligence optimization for wind speed prediction
- (2021) Yuanyuan Shao et al. APPLIED MATHEMATICAL MODELLING
- Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling
- (2021) Wei Li et al. ENERGY
- Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction
- (2020) Xue-Bo Jin et al. Mathematics
- A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting
- (2020) Pei Du et al. APPLIED SOFT COMPUTING
- Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles
- (2020) Jia-Hong Tang et al. Atmospheric Pollution Research
- Assessing spatiotemporal air environment degradation and improvement represented by PM2.5 in China using two-phase hybrid model
- (2020) Kun Yang et al. Sustainable Cities and Society
- A hybrid framework for forecasting PM2.5 concentrations using multi-step deterministic and probabilistic strategy
- (2019) Hui Liu et al. Air Quality Atmosphere and Health
- Short-term PM2.5 forecasting based on CEEMD-RF in five cities of China
- (2019) Da Liu et al. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
- PM 2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors
- (2018) Suling Zhu et al. ATMOSPHERIC ENVIRONMENT
- Influence of sulfur dioxide on the respiratory system of Miyakejima adult residents 6 years after returning to the island
- (2017) Takeshi Kochi et al. JOURNAL OF OCCUPATIONAL HEALTH
- Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York
- (2017) et al. Atmosphere
- Forecasting wind speed using empirical mode decomposition and Elman neural network
- (2014) Jujie Wang et al. APPLIED SOFT COMPUTING
- Explorative forecasting of air pollution
- (2014) D. Domańska et al. ATMOSPHERIC ENVIRONMENT
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started