Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

Moursi, Ahmed Samy AbdElAziz; El-Fishawy, Nawal; Djahel, Soufiene; Shouman, Marwa A.

doi:10.3390/s22124418

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…[ 125 ] 2022 Beijing, China CBAM-CNN-BiLSTM H/M/T+(13-18) 31.47 21.86 - 0.81 H/M/T+(25-48) 32.34 22.30 - 0.79 Moursi et al. [ 126 ] 2022 Beijing, China NARX-CNN-LSTM H/S/T+1 23.64 - - 0.92 Zhu et al. [ 127 ] 2023 Shanghai, China 1D-CNN + BiLSTM H/S/T+1 3.88 2.52 - 0.94 Pak et al.…”

Section: Methods Reviewmentioning

confidence: 99%

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

Zhou,

Wang,

Zhu

et al. 2024

Environmental Science and Ecotechnology

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Section: Methods Reviewmentioning

confidence: 99%

“…Moursi et al. [ 126 ] proposed a combined CNN and LSTM hybrid model based on a nonlinear autoregressive network with exogenous inputs (NARX) for enhancing PM 2.5 prediction. Zhu 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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“…The function f approximates the behavior of the TG. NARX can be implemented using a feedforward neural network to approximate the function

[ 26 ].…”

Section: The Narx Modelmentioning

confidence: 99%

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

Aquize

Cajahuaringa

Machuca

et al. 2023

Sensors

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The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10−5, RMSE of 0.4411% and a MAPE of 0.0643.

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“…The function of the convolution layer is to extract the feature of input data and then reduce the dimension of the feature extracted by the convolution layer through the pooling layer to further extract the feature. Finally, the training results of the model are output through the complete connection layer and the output layer [33,34]. The mathematical expression of CNN model convolution process is:…”

Section: Cnnmentioning

confidence: 99%

Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model

et al. 2023

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The problem of dust pollution in the open-pit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the open-pit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the open-pit coal mine. It is of practical significance to establish a prediction model for scientific and effective prediction of road dust concentration in the open pit coal mine. The prediction model helps reduce dust hazards. This paper uses the hourly air quality and meteorological data of an open-pit coal mine in Tongliao City, Inner Mongolia Autonomous Region, from January 1, 2020, to December 31, 2021. Create a CNN-BiLSTM-Attention multivariate hybrid model consisting of a Convolutional Neural Network (CNN), a bidirectional long short-term memory neural network (BiLSTM), and an attention mechanism, Prediction of PM2.5 concentration in the next 24h. Establish prediction models of parallel and serial structures, and carry out many experiments according to the change period of the data to determine the optimal configuration and the input and output size. Then, a comparison of the proposed model and Lasso regression, SVR, XGBoost, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models for short-term prediction (24h) and long-term prediction (48h, 72h, 96h, and 120h). The results show that the CNN-BiLSTM-Attention multivariate mixed model proposed in this paper has the best prediction performance. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the short-term forecast (24h) are 6.957, 8.985, and 0.914, respectively. Evaluation indicators of long-term forecasts (48h, 72h, 96h, and 120h) are also superior to contrast models. Finally, we used field-measured data to verify, and the obtained evaluation indexes MAE, RMSE, and R2 are 3.127, 3.989, and 0.951, respectively. The model-fitting effect was good.

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Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

Cited by 17 publications

References 42 publications

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

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

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model

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