PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China DOI Creative Commons

Rongkun Zou,

Heyun Huang,

Xiaoman Lu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1915 - 1915

Published: May 27, 2024

As the urgency of PM2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used forecasting concentration trends and other atmospheric pollutants. Traditional time-series models, like long short-term memory (LSTM) temporal convolutional network (TCN), were found to be efficient pollutant estimation, but either model accuracy was not high enough or models encountered certain challenges due their own structure some specific application scenarios. This study proposed a high-accuracy, hourly model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by methods, based on air data meteorological data, aerosol optical depth (AOD) retrieved from Himawari-8 satellite. research Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020–2022. The PD-LL-Transformer had three parts: embedding layer, which integrated advantages allocating multi-variate features more refined manner combined superiority different processing methods; block, LSTM TCN; Transformer encoder block. Over test set (the whole year 2022), model’s R2 0.8929, mean absolute error (MAE) 4.4523 µg/m3, root squared (RMSE) 7.2683 showing great prediction. surpassed existing upon same tasks similar datasets, with help tool better performance applicability could established.

Language: Английский

Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector DOI
Majid Emami Javanmard, Yidan Tang,

Zhongjie WANG

et al.

Applied Energy, Journal Year: 2023, Volume and Issue: 338, P. 120830 - 120830

Published: March 20, 2023

Language: Английский

Citations

69
Air pollutant prediction based on ARIMA-WOA-LSTM model DOI Creative Commons
Jun Luo,

Yaping Gong

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(6), P. 101761 - 101761

Published: April 21, 2023

The problem of air pollution has always plagued people's lives, and the management cannot be achieved without prediction assessment concentration various pollutants. In this paper, we propose a method to accurately predict pollutants with aim ensuring efficiency management. proposed ARIMA-WOA-LSTM model uses ARIMA extract linear part data output nonlinear part, while WOA-LSTM is used where whale algorithm find perfect hyperparameters for LSTM, objectives search include number neurons, learning rate batch length. To prove excellence developed in article compared ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, LSTM. results show that performs better than other models three aspects: pollutant accuracy, stability; combined also much single aspects; excellent five which important error reduction model. high reference

Language: Английский

Citations

66
The Explainable Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing VOCs’ Environmental Fate DOI Creative Commons
Luka Jovanovic, Gordana Jovanović, Mirjana Perišić

et al.

Atmosphere, Journal Year: 2023, Volume and Issue: 14(1), P. 109 - 109

Published: Jan. 4, 2023

In this paper, we explore the computational capabilities of advanced modeling tools to reveal factors that shape observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, meteorological parameters obtained from Global Data Assimilation System. order determine model will be capable achieving a superior level performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while relative SHapley Additive exPlanations values used estimate importance each pollutant parameter prediction concentrations. According results, are mostly shaped by toluene finest aerosol fraction concentrations, in environment governed temperature, volumetric soil moisture content, momentum flux direction, as well hydrocarbons nitrogen oxide. types conditions which provided impact aerosol, temperature dynamics distinguished described.

Language: Английский

Citations

59
Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban PM2.5 concentration prediction of India’s polluted cities DOI
Subham Kumar, Vipin Kumar

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141259 - 141259

Published: Feb. 14, 2024

Language: Английский

Citations

19
A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic DOI Creative Commons
Zixi Zhao, Jinran Wu, Fengjing Cai

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Jan. 18, 2023

Abstract China implemented a strict lockdown policy to prevent the spread of COVID-19 in worst-affected regions, including Wuhan and Shanghai. This study aims investigate impact these lockdowns on air quality index (AQI) using deep learning framework. In addition historical pollutant concentrations meteorological factors, we incorporate social spatio-temporal influences particular, spatial autocorrelation (SAC), which combines temporal with correlation, is adopted reflect influence neighbouring cities data. Our analysis obtained estimates effects as − 25.88 20.47 The corresponding prediction errors are reduced by about 47% for 67% Shanghai, enables much more reliable AQI forecasts both cities.

Language: Английский

Citations

41
Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer DOI Creative Commons

Jiahui Duan,

Yaping Gong,

Jun Luo

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 26, 2023

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient accurate air quality prediction model would help to manage the problem. In this paper, we build combined accurately predict AQI based on real data from four cities. First, use ARIMA fit linear part of CNN-LSTM non-linear avoid blinding in hyperparameter setting. Then, dilemma setting, Dung Beetle Optimizer algorithm find hyperparameters model, determine optimal hyperparameters, check accuracy model. Finally, compare proposed with nine other widely used models. The experimental results show paper outperforms comparison models terms root mean square error (RMSE), absolute (MAE) coefficient determination (R 2 ). RMSE values for cities were 7.594, 14.94, 7.841 5.496; MAE 5.285, 10.839, 5.12 3.77; R 0.989, 0.962, 0.953 respectively.

Language: Английский

Citations

40
Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation DOI Creative Commons

Mohammad Ehteram,

Ali Najah Ahmed, Zohreh Sheikh Khozani

et al.

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(6), P. 101766 - 101766

Published: April 18, 2023

Language: Английский

Citations

37
Heavy metals contamination status and health risk assessment of indoor and outdoor dust in Ahvaz and Zabol cities, Iran DOI

Seyed Reza Asvad,

Abbas Esmaili‐Sari,

Nader Bahramifar

et al.

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(4), P. 101727 - 101727

Published: March 22, 2023

Language: Английский

Citations

28
Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction DOI

Ying Ren,

Siyuan Wang, Bisheng Xia

et al.

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(4), P. 101703 - 101703

Published: March 4, 2023

Language: Английский

Citations

27
Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China DOI Creative Commons

Zhiyang Zhao,

Mengmeng Zhai,

Guohua Li

et al.

BMC Infectious Diseases, Journal Year: 2023, Volume and Issue: 23(1)

Published: Feb. 6, 2023

Abstract Background Influenza is an acute respiratory infectious disease that highly and seriously damages human health. Reasonable prediction of great significance to control the epidemic influenza. Methods Our data were extracted from Shanxi Provincial Center for Disease Control Prevention. Seasonal-trend decomposition using Loess (STL) was adopted analyze season characteristics influenza in Province, China, 1st week 2010 52nd 2019. To handle insufficient performance seasonal autoregressive integrated moving average (SARIMA) model predicting nonlinear parts poor accuracy directly original sequence, this study established SARIMA model, combination Long-Short Term Memory neural network (SARIMA-LSTM) SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) make predictions identify best model. Additionally, Mean Squared Error (MSE), Absolute (MAE) Root (RMSE) used evaluate models. Results The time series Province 2019 showed a year-by-year decrease with obvious characteristics. peak period mainly concentrated end year beginning next year. fitting SSA-SARIMA-LSTM Compared MSE, MAE RMSE decreased by 38.12, 17.39 21.34%, respectively, performance; 42.41, 18.69 24.11%, performances. Furthermore, compared 28.26, 14.61 15.30%, 36.99, 7.22 20.62%, Conclusions performances better than those Generally speaking, we can apply influenza, offer leg-up public policy.

Language: Английский

Citations

26