An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution’s negative impact on the environment and people’s health. The data-driven forecasting model can help a better understanding of environmental air quality. The existing data-driven forecasting mo...

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Bibliographic Details
Published in:Sustainable cities and society Vol. 70; p. 102923
Main Authors: Samal, K. Krishna Rani, Panda, Ankit Kumar, Babu, Korra Sathya, Das, Santos Kumar
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2021
Subjects:
Convolutional LSTM
Deep learning
Imputation
PM2.5
Sparse Denoising Autoencoder
Time series forecasting
Deep learning
Time series forecasting
Imputation
Sparse Denoising Autoencoder
Convolutional LSTM
PM2.5
ISSN:2210-6707, 2210-6715
Online Access:Get full text
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Summary:Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution’s negative impact on the environment and people’s health. The data-driven forecasting model can help a better understanding of environmental air quality. The existing data-driven forecasting models usually ignore missing values, the correlations between the pollutant and meteorological factors and fail to perform temporal modeling effectively, affecting prediction accuracy. In response to these issues, we present a deep learning-based Convolutional LSTM–SDAE (CLS) model to forecast the particulate matter level, revealing the correlation between particulate matter and meteorological factors. In the proposed architecture, the k nearest neighbor (KNN) imputation technique is employed to recover the air quality dataset’s missing values. The Convolutional Long Short Term Memory (CNN–LSTM) unit identifies the vast dataset’s hidden features and performs pollutants’ temporal modeling. In addition, Bidirectional Gatted Recurrent Unit (BIGRU) is implemented as both encoder and decoder in Sparse Denoising Autoencoder, which reconstructs the CNN–LSTM model’s output in the dynamic fine-tuning layer to get robust prediction results. The experimental results in Talcher, India, and Beijing, China indicate that the model can improve forecasting accuracy and outperforms the other state of art and baseline models. •Meteorological factors have an impact on PM2.5 that adversely affects the human body.•Handling missing values of air quality can significantly affect forecasting results.•The experimental results show that the proposed CLS model has better forecasting performance than other baseline models.
ISSN:2210-6707
2210-6715
DOI:10.1016/j.scs.2021.102923