A hybrid deep learning technology for PM2.5 air quality forecasting

Zhang, Zhendong; Zeng, Yongkang; Yan, Ke

doi:10.1007/s11356-021-12657-8

Cited by 94 publications

(34 citation statements)

References 31 publications

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“…This increases the amount of information available to the network, improving the context available to the network. Again, these findings are consistent with prior literature where Bidirectional-LSTM had performed better than its counterparts (Zhang et al, 2021). However, the Zhang et al (2021) study only investigated the bidirectional-LSTM model, and other recurrent neural network models were not investigated.…”

Section: Discussionsupporting

confidence: 89%

“…Again, these findings are consistent with prior literature where Bidirectional-LSTM had performed better than its counterparts (Zhang et al, 2021). However, the Zhang et al (2021) study only investigated the bidirectional-LSTM model, and other recurrent neural network models were not investigated. In our experimentation, we investigated a wide variety of RNN models by calibrating the models using the grid-search technique.…”

Section: Discussionsupporting

confidence: 89%

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Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

Saini

Chaturvedi

Dutt

2021

Front. Environ. Sci.

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Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 89%

Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

Saini

Chaturvedi

Dutt

2021

Front. Environ. Sci.

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“…One of the future works of this study is to extend the existing work for more generalized datasets, i.e., achieving acceptable forecasting results for longer-term forecasting of the PV system energy generation. Another future work direction is to apply the proposed framework towards a broader range of time series data applications in other fields, such as air quality forecasting [14,35] and energy consumption forecasting [36].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Deep Learning Enhanced Solar Energy Forecasting with AI‐Driven IoT

Zhou

Liu

Yan

et al. 2021

Wireless Communications and Mobile Computing

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Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.

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“…e mixing mode problem refers to the presence of extremely similar oscillations in different modes or very disparate amplitude in a mode. By adding Gaussian white noise to the signal, the ensemble empirical mode decomposition (EEMD) algorithm largely eliminates the mixing mode problem in the EMD algorithm [30]. However, the EEMD algorithm cannot completely eliminate Gaussian white noise after signal reconstruction, which causes reconstruction errors.…”

Section: Ceemdanmentioning

confidence: 99%

Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data

Cao

Zhou

Yan

2021

Mathematical Problems in Engineering

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Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.

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A hybrid deep learning technology for PM2.5 air quality forecasting

Cited by 94 publications

References 31 publications

Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

Deep Learning Enhanced Solar Energy Forecasting with AI‐Driven IoT

Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data

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