A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks

Xu, Shenyi; Li, Wei; Zhu, Yanyan; Xu, Aiting

doi:10.1038/s41598-022-17754-3

Cited by 31 publications

(8 citation statements)

References 78 publications

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“…[ 97 ] 2022 Jing-Jin-Ji Region, China LSTM-CEEMADN D/S/T+1 3.52 2.73 - 0.97 Xu et al. [ 98 ] 2022 China CEEMD-CNN-LSTM H/S/T+2 12.67 9.60 - 0.87 Zhou et al. [ 99 ] 2022 Chongqing, China Kalman-Filter-LSTM H/S/T+1 8.45 7.30 - 0.96 Zhao et al.…”

Section: Methods Reviewmentioning

confidence: 99%

See 1 more Smart Citation

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%

“…Xu et al. [ 98 ] and Zhang et al. [ 101 ] proposed improved CEEMD-LSTM models combined with CNN and FCN, respectively.…”

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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“…It addresses long-term dependency problem which cause vanishing gradient problem in the RNN model. LSTM introduces three gates: forget gate, input gate, and output gate; those gates control the network memorizing process: read, store, and write historical information [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations

Shehhi

Welsch

2023

J Big Data

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Nitrogen Dioxide (NO$$_{2}$$ 2 ) is a common air pollutant associated with several adverse health problems such as pediatric asthma, cardiovascular mortality,and respiratory mortality. Due to the urgent society’s need to reduce pollutant concentration, several scientific efforts have been allocated to understand pollutant patterns and predict pollutants’ future concentrations using machine learning and deep learning techniques. The latter techniques have recently gained much attention due it’s capability to tackle complex and challenging problems in computer vision, natural language processing, etc. In the NO$$_{2}$$ 2 context, there is still a research gap in adopting those advanced methods to predict the concentration of pollutants. This study fills in the gap by comparing the performance of several state-of-the-art artificial intelligence models that haven’t been adopted in this context yet. The models were trained using time series cross-validation on a rolling base and tested across different periods using NO$$_{2}$$ 2 data from 20 monitoring ground-based stations collected by Environment Agency- Abu Dhabi, United Arab Emirates. Using the seasonal Mann-Kendall trend test and Sen’s slope estimator, we further explored and investigated the pollutants trends across the different stations. This study is the first comprehensive study that reported the temporal characteristic of NO$$_{2}$$ 2 across seven environmental assessment points and compared the performance of the state-of-the-art deep learning models for predicting the pollutants’ future concentration. Our results reveal a difference in the pollutants concentrations level due to the geographic location of the different stations, with a statistically significant decrease in the NO$$_{2}$$ 2 annual trend for the majority of the stations. Overall, NO$$_{2}$$ 2 concentrations exhibit a similar daily and weekly pattern across the different stations, with an increase in the pollutants level during the early morning and the first working day. Comparing the state-of-the-art model performance transformer model demonstrate the superiority of ( MAE:0.04 (± 0.04),MSE:0.06 (± 0.04), RMSE:0.001 (± 0.01), R$$^{2}$$ 2 : 0.98 (± 0.05)), compared with LSTM (MAE:0.26 (± 0.19), MSE:0.31 (± 0.21), RMSE:0.14 (± 0.17), R$$^{2}$$ 2 : 0.56 (± 0.33)), InceptionTime (MAE: 0.19 (± 0.18), MSE: 0.22 (± 0.18), RMSE:0.08 (± 0.13), R$$^{2}$$ 2 :0.38 (± 1.35) ), ResNet (MAE:0.24 (± 0.16), MSE:0.28 (± 0.16), RMSE:0.11 (± 0.12), R$$^{2}$$ 2 :0.35 (± 1.19) ), XceptionTime (MAE:0.7 (± 0.55), MSE:0.79 (± 0.54), RMSE:0.91 (± 1.06), R$$^{2}$$ 2 : $$-$$ - 4.83 (± 9.38) ), and MiniRocket (MAE:0.21 (± 0.07), MSE:0.26 (± 0.08), RMSE:0.07 (± 0.04), R$$^{2}$$ 2 : 0.65 (± 0.28) ) to tackle this challenge. The transformer model is a powerful model for improving the accurate forecast of the NO$$_{2}$$ 2 levels and could strengthen the current monitoring system to control and manage the air quality in the region.

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“…These measures are mathematically represented by Eqs. ( 8)- (10) as described in [22][23][24][25][26][27][28][29][30][31][32]. These error measures allow for a comprehensive evaluation of the performance of the prediction models, providing a clear understanding of their strengths and weaknesses.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Dahmani,

Ammi,

Bailek

et al. 2023

CMC

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Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world's energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic zones were used to develop the model. Various error measurements were used to determine the accuracy of the prediction models, including the correlation coefficient, the mean absolute error, and the root mean square error (RMSE). The optimal support vector machine (SVM) model showed exceptional

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A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks

Cited by 31 publications

References 78 publications

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

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

Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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