Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning-Reference-Cited by-同舟云学术

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

Published:2022-03-26 Issue:10 Volume:36 Page:3417-3437
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Wang Jujie,Xu Wenjie,Dong Jian,Zhang Yue

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-022-02202-5.pdf

Reference66 articles.

1. Ahmed AAM, Deo RC, Ghahramani A, Raj N, Feng Q, Yin Z et al (2021) LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stoch Environ Res Risk Assess 35:1851–1881

2. Bai Y, Zeng B, Li C, Zhang J (2019a) An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting. Chemosphere 222:286–294

3. Bai Y, Li Y, Zeng B, Li C, Zhang J (2019b) Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality. J Clean Prod 224:739–750

4. Barzegar R, Aalami MT, Adamowski J (2020) Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stoch Environ Res Risk Assess 34:415–433

5. Biancofiore F, Busilacchio M, Verdecchia M, Tomassetti B, Aruffo E, Bianco S et al (2017) Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos Pollut Res 8:652–659

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