Air quality forecasting with hybrid LSTM and extended stationary wavelet transform

Zeng, Yongkang, Chen, Jingjing, Jin, Ning, Jin, Xiaoping, and Du, Yang (2022) Air quality forecasting with hybrid LSTM and extended stationary wavelet transform. Building and Environment, 213. 108822.

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Air quality measurements and forecasting is one of the most popular research topics in the field of sustainable intelligent environmental design, urban area development and pollution control, especially for Asia developing countries, such as China. Deep learning (DL) technologies for time series data forecasting, such as the recurrent neural network (RNN) and long short term memory (LSTM) neural network, have attracted extensive attentions in recent years and have been applied to AQI forecasting. However, two problems exist in the literature. First, the volatility of the AQI data causes difficulties for singular DL models to produce reliable forecasting results. Second, a long history of the air-quality data is required in the training stage, which is usually unavailable. A novel forecasting model that integrates the extended stationary wavelet transform (ESWT) and the nested long short-term memory (NLSTM) neural network for PM2.5 air quality forecasting is proposed in this study. The results show that the proposed method outperforms state-of-art forecasting methods and recently published works in terms of different error metrics, such as absolute error, R2, MAE, RMSE, and MAPE.

Item ID: 73785
Item Type: Article (Research - C1)
ISSN: 1873-684X
Keywords: Air quality forecasting, Nested long short term memory, Wavelet transform
Copyright Information: © 2022 Elsevier Ltd. All rights reserved.
Research Data:
Date Deposited: 11 May 2022 09:00
FoR Codes: 40 ENGINEERING > 4011 Environmental engineering > 401101 Air pollution modelling and control @ 30%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 70%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1801 Air quality, atmosphere and weather > 180101 Air quality @ 100%
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