A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction

Zhang, Bo; Zou, Guojian; Qin, Dongming; Lu, Yunjie; Jin, Yupeng; Wang, Hui

doi:10.1016/j.scitotenv.2020.144507

Cited by 70 publications

(24 citation statements)

References 47 publications

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“…e A&A learning has attached adequate importance to time factors and we reformed the input structure of the attention mechanism. In comparison with other attention-based methods, the performance of A&A learning on the dataset of Shanghai proves better than all methods [39] when the segmentation is [23] and [72] and the result is shown in Table 6.…”

Section: Discussionmentioning

confidence: 99%

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Zhang

Jin

Zou

et al. 2021

Mathematical Problems in Engineering

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Air pollution has become a critical issue in human’s life. Predicting the changing trends of air pollutants would be of great help for public health and natural environments. Current methods focus on the prediction accuracy and retain the forecasting time span within 12 hours. Shorter time span decreases the practicability of these perditions, even with higher accuracy. This study proposes an attention and autoencoder (A&A) hybrid learning approach to obtain a longer period of air pollution changing trends while holding the same high accuracy. Since pollutant concentration forecast highly relates to time changing, quite different from normal prediction problems like autotranslation, we integrate “time decay factor” into the traditional attention mechanism. The time decay factor can alleviate the impact of the value observed from a longer time before while increasing the impact of the value from a closer time point. We also utilize the hidden states in the decoder to build connection between history values and current ones. Thus, the proposed model can extract the changing trend of a longer history time span while coping with abrupt changes within a shorter time span. A set of experiments demonstrate that the A&A learning approach can obtain the changing trend of air pollutants, like PM2.5, during a longer time span of 12, 24, or even 48 hours. The approach is also tested under different pollutant concentrations and different periods and the results validate its robustness and generality.

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Section: Discussionmentioning

confidence: 99%

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Zhang

Jin

Zou

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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“…it achieved a successful performance versus other methods in the literature, specifically in the area of text translation [50]. Recently, the Encoder-Decoder long short-term memory (LSTM) has been applied for several time series forecasting tasks, such as power consumption [51], metal temperature [52], air pollutant [53] behaviour prediction [54], and gas concentration [55]. However, the LSTM core for Encoder-Decoder architecture needs to be developed using recent deep units.…”

Section: Review Of Predictions Modelsmentioning

confidence: 99%

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Shahin

Almotairi

2021

Fractal Fract

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The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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“…There is a complex interaction between moisture changes and meteorological factors in these objects, and only by fully extracting the features of this complex relationship can reliable prediction results be obtained [18,19]. According to relevant studies, the moisture content is a dynamic and continuous process in the time dimension, i.e., there is an interaction between the moisture content of adjacent moments [20]. The mechanism of change of moisture content in coal is similar to that of soil and other substances, which is susceptible to the influence of meteorological factors such as temperature and humidity, and shows dynamic continuity in the trend of change [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel ABRM Model for Predicting Coal Moisture Content

Zhai

et al. 2022

J Intell Robot Syst

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Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

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A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction

Cited by 70 publications

References 47 publications

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

A Novel ABRM Model for Predicting Coal Moisture Content

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