Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

Fu, Minglei; Le, Caowei; Fan, Tingchao; Prakapovich, Ryhor; Manko, Dmytro; Dmytrenko, Oleh; Lande, Dmytro; Shahid, Shamsuddin; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s11356-021-15574-y

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…Luo et al researched the trends and seasonality of PM2.5 concentrations by EMD in the US and evaluated the simulation results of the air quality model [22]. The method was also used to predict particulate matter concentration by Fu et al and achieved a prediction accuracy of approximately 80% [23]. The spatial and temporal patterns and trends show that the AQI values have gradually been decreasing in recent years.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

Zhang

et al. 2021

Sustainability

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Air pollution is a serious problem in China, and the government has taken a series of steps to solve it. However, it is still u2nclear how the situation has improved after years of atmospheric pollution control. Shandong Province, which has the second largest population and the highest coal consumption in China, was chosen to analyze the spatiotemporal air quality trends. We obtained daily air quality index (AQI) values from 91 stations in the province from 1 January 2014, to 31 December 2019, based on an adaptive data analysis method, empirical mode decomposition (EMD). The distribution of AQI in Shandong Province was heterogeneous at both spatial and temporal scales. All the stations could be divided into four clusters whose AQI trends decreased from 75 to 53, 95 to 68, 128 to 82, and 148 to 82, respectively. The overall trend of pollution became more serious from east to west in the province. The AQI is the largest in winter, followed by spring and autumn, and the smallest index occurs in summer. There are four types of annual trends of the AQI of each city. The overall downward trend indicates that the air quality of each city was improving annually.

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Section: Discussionmentioning

confidence: 99%

An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

Zhang

et al. 2021

Sustainability

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“…[ 92 ] 2022 Shanghai, China EMD-SE-BiLSTM H/S/T+1 2.77 1.88 - 0.98 H/S/T+3 5.04 3.56 - 0.95 Fu et al. [ 93 ] 2021 Hangzhou, China CEEMD-LSTM H/S/T+1 6.48 4.76 15.76 - Zhang et al. [ 94 ] 2020 Gansu, China ESN-PSO H/S/T+1 8.73 5.47 8.20 0.93 Wang et al.…”

Section: Methods Reviewmentioning

confidence: 99%

Deep-learning architecture for PM2.5 concentration prediction: A review

Zhou,

Wang,

Zhu

et al. 2024

Environmental Science and Ecotechnology

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“…LSTM network is one of the deep learning techniques that has been proposed to mitigate the vanishing/exploding gradient problems related to RNN networks. The LSTM uses a memory unit to store dependent information in consecutive data for a long horizon time (Moradzadeh et al 2020 ; Fu et al 2021 ). Nonlinear data analysis, regression, estimation of the relationship between input and output variables, and classification are the salient applications of LSTM.…”

Section: Methodologiesmentioning

confidence: 99%

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Moradzadeh,

Moayyed,

Mohammadi-Ivatloo

et al. 2023

Environ Sci Pollut Res

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Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.

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Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

Cited by 13 publications

References 39 publications

An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

Deep-learning architecture for PM2.5 concentration prediction: A review

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

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