A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network

Precise and efficient air quality prediction plays a vital role in safeguarding public health and informing policy-making. Fine particulate matter, specifically PM2.5 and PM10, serves as a crucial indicator for assessing and managing air pollution levels. In this paper, a daily pollution concentrati...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2023-07, Vol.15 (13), p.10660
Main Authors: Huang, Zhong, Li, Linna, Ding, Guorong
Format: Article
Language:English
Subjects:
Accuracy
Air pollution
Air quality
Algorithms
Analysis
Bayesian analysis
Datasets
Environmental monitoring
Forecasting
Forecasts and trends
Learning
Long short-term memory
Mathematical models
Neural networks
Optimization
Outdoor air quality
Particulate emissions
Particulate matter
Pollutants
Pollution levels
Prediction models
Public health
Signal processing
Supervised learning
Support vector machines
Time series
Trends
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Precise and efficient air quality prediction plays a vital role in safeguarding public health and informing policy-making. Fine particulate matter, specifically PM2.5 and PM10, serves as a crucial indicator for assessing and managing air pollution levels. In this paper, a daily pollution concentration prediction model combining successive variational mode decomposition (SVMD) and a bidirectional long short-term memory (BiLSTM) neural network is proposed. Firstly, SVMD is used as an unsupervised feature-learning method to divide data into intrinsic mode functions (IMFs) and to extract frequency features and improve short-term trend prediction. Secondly, the BiLSTM network is introduced for supervised learning to capture small changes in the air pollutant sequence and perform prediction of the decomposed sequence. Furthermore, the Bayesian optimization (BO) algorithm is employed to identify the optimal key parameters of the BiLSTM model. Lastly, the predicted values are reconstructed to generate the final prediction results for the daily PM2.5 and PM10 datasets. The prediction performance of the proposed model is validated using the daily PM2.5 and PM10 datasets collected from the China Environmental Monitoring Center in Tianshui, Gansu, and Wuhan, Hubei. The results show that SVMD can smooth the original series more effectively than other decomposition methods, and that the BO-BiLSTM method is better than other LSTM-based models, thereby proving that the proposed model has excellent feasibility and accuracy.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151310660