Temporal convolutional denoising autoencoder network for air pollution prediction with missing values
In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However,...
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| Veröffentlicht in: | Urban climate Jg. 38; S. 100872 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.07.2021
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| ISSN: | 2212-0955, 2212-0955 |
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| Abstract | In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However, air quality modeling faces challenges like long-term pollutant variations due to meteorological variables impact and missing values due to natural disaster or sensor shutdown. These issues make air quality models more complex and challenging to introspect. So, we developed Temporal Convolutional Denoising Autoencoder (TCDA) network, a hybrid PM2.5 prediction framework that can perform rapid extraction of complex dataset's features, handle missing values and improve PM2.5 prediction results. This research paper includes Temporal Convolutional Network (TCN) and Denoising Autoencoder (DAE) network to handle nonlinear multivariate massive datasets. The former utilizes parallel feature processing of Convolutional Neural Network (CNN) and temporal component modeling ability of Recurrent Neural Network (RNN), which helps for features extraction from complex dataset. The latter is to reconstruct the error to fine-tune the prediction results and handle missing values. The proposed model's prediction capability is evaluated by comparing its performance with other baseline models, which shows its performance superiority over other models.
•Handling missing values has a significant role in improving pollution prediction results.•Data imputation and forecasting are two major steps towards proper air pollution management.•Temporal Convolutional Denoising Autoencoder can handle different patterns of missing values to predict PM2.5 concentration. |
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| AbstractList | In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However, air quality modeling faces challenges like long-term pollutant variations due to meteorological variables impact and missing values due to natural disaster or sensor shutdown. These issues make air quality models more complex and challenging to introspect. So, we developed Temporal Convolutional Denoising Autoencoder (TCDA) network, a hybrid PM2.5 prediction framework that can perform rapid extraction of complex dataset's features, handle missing values and improve PM2.5 prediction results. This research paper includes Temporal Convolutional Network (TCN) and Denoising Autoencoder (DAE) network to handle nonlinear multivariate massive datasets. The former utilizes parallel feature processing of Convolutional Neural Network (CNN) and temporal component modeling ability of Recurrent Neural Network (RNN), which helps for features extraction from complex dataset. The latter is to reconstruct the error to fine-tune the prediction results and handle missing values. The proposed model's prediction capability is evaluated by comparing its performance with other baseline models, which shows its performance superiority over other models.
•Handling missing values has a significant role in improving pollution prediction results.•Data imputation and forecasting are two major steps towards proper air pollution management.•Temporal Convolutional Denoising Autoencoder can handle different patterns of missing values to predict PM2.5 concentration. |
| ArticleNumber | 100872 |
| Author | Babu, Korra Sathya Das, Santos Kumar Samal, K. Krishna Rani |
| Author_xml | – sequence: 1 givenname: K. Krishna Rani surname: Samal fullname: Samal, K. Krishna Rani email: 517cs6019@nitrkl.ac.in organization: Department of Computer Science and Engineering, National Institute of Technology, Rourkela 769008, India – sequence: 2 givenname: Korra Sathya surname: Babu fullname: Babu, Korra Sathya organization: Department of Computer Science and Engineering, National Institute of Technology, Rourkela 769008, India – sequence: 3 givenname: Santos Kumar surname: Das fullname: Das, Santos Kumar organization: Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela 769008, India |
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| Keywords | Time series prediction LSTM Temporal convolutional network Autoencoder PM2.5 |
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