Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China

Saif-ul-Allah, Muhammad Waqas; Qyyum, Muhammad Abdul; Ul‐Haq, Noaman; Salman, Chaudhary Awais; Ahmed, Faisal

doi:10.3389/fenvs.2021.816616

Cited by 9 publications

(2 citation statements)

References 31 publications

(29 reference statements)

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“…Belachsen et al, 2022 [18] proposed a multivariate KNN technique for half-hourly pm2.5 time series reaching an NMAE between 0.21 and 0.26. Saif-ul-Allah et al, 2022 [19] proposed the recurrent neural network known as GRU, reaching an RMSE of 10.60 ug/m 3 and surpassing other models such as SVM, LSTM, and BiLSTM. Alkabbani et al, 2022 [20] proposed a multivariate random forest model for pm2.5 time series imputation, and the results were compared only with linear interpolation, showing that random forest achieves the best RMSE (3.756 ug/m 3 ).…”

Section: Related Workmentioning

confidence: 99%

Pm2.5 Time Series Imputation with Deep Learning and Interpolation

Flores,

Tito-Chura,

Centty-Villafuerte

et al. 2023

Computers

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Commonly, regression for time series imputation has been implemented directly through regression models, statistical, machine learning, and deep learning techniques. In this work, a novel approach is proposed based on a classification model that determines the NA value class, and from this, two types of interpolations are implemented: polynomial or flipped polynomial. An hourly pm2.5 time series from Ilo City in southern Peru was chosen as a study case. The results obtained show that for gaps of one NA value, the proposal in most cases presents superior results to techniques such as ARIMA, LSTM, BiLSTM, GRU, and BiGRU; thus, on average, in terms of R2, the proposal exceeds implemented benchmark models by between 2.4341% and 19.96%. Finally, supported by the results, it can be stated that the proposal constitutes a good alternative for short-gaps imputation in pm2.5 time series.

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Section: Related Workmentioning

confidence: 99%

Pm2.5 Time Series Imputation with Deep Learning and Interpolation

Flores,

Tito-Chura,

Centty-Villafuerte

et al. 2023

Computers

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“…Saif-ul-Allah et al compared different missing data imputation techniques and reported project to model plan (PMP) as outperforming method for PM2.5. This study employed PMP as an imputation technique and used deep learning methods LSTM, Bi-LSTM, and GRU to predict PM2.5 concentration in Guangzhou city, China (Saif-ul-Allah et al, 2022). Moreover, Fast Fourier Transformation (FFT) was employed to analyze the frequency component of each variable with respect to time.…”

Section: Process Descriptionmentioning

confidence: 99%

Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

et al. 2022

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Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.

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