A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction
Copyright © 2022 Elsevier Inc. All rights reserved..
With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:215 |
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| Enthalten in: |
Environmental research - 215(2022), Pt 1 vom: 01. Dez., Seite 114228 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ji, Chunlei [VerfasserIn] |
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| Links: |
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| Themen: |
CEEMDAN |
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| Anmerkungen: |
Date Completed 21.11.2022 Date Revised 12.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.envres.2022.114228 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM346016916 |
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| 100 | 1 | |a Ji, Chunlei |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction |
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| 500 | |a Date Completed 21.11.2022 | ||
| 500 | |a Date Revised 12.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Elsevier Inc. All rights reserved. | ||
| 520 | |a With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a CEEMDAN | |
| 650 | 4 | |a Long short-term memory | |
| 650 | 4 | |a Regularized extreme learning machine | |
| 650 | 4 | |a Whale optimization algorithm | |
| 700 | 1 | |a Zhang, Chu |e verfasserin |4 aut | |
| 700 | 1 | |a Hua, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Huixin |e verfasserin |4 aut | |
| 700 | 1 | |a Nazir, Muhammad Shahzad |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Tian |e verfasserin |4 aut | |
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