Predicting concentration levels of air pollutants by transfer learning and recurrent neural network

Fong, Iat Hang; Li, Tengyue; Fong, Simon; Wong, Raymond K.; Tallón‐Ballesteros, Antonio J.

doi:10.1016/j.knosys.2020.105622

Cited by 51 publications

(21 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Li et al in their study [39] found out that the proposed model predicts better PM 2.5 than NO x , as NO x is highly reactive and has larger temporal variability. Therefore, many studies mentioned the implementation of the proposed model for predicting other pollutants as future work [21,40]. Another limitation is the lack of data in spatiotemporal resolution [41,42].…”

Section: Resultsmentioning

confidence: 99%

“…Including other datasets such as aerosol optical depth data and meteorological data can help to overcome this issue [45]. It might also be useful to apply techniques for handling imbalanced datasets [40]. Another limitation that we have already mentioned is a prediction with the long temporal resolution since due to the accumulated error, the accuracy decreases as the temporal resolution increases [46,47].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

View full text Add to dashboard Cite

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

View full text Add to dashboard Cite

show abstract

“…The effectiveness of LSTM in comparing the performances of LSTM RNN by initialized methods of learning transmission and randomly initialized recurrent neural networks was confirmed by Fong et al [46]. Another variant called CTS-LSTM for the collective prediction of correlated time series with the aim of improving the predictive accuracy of the model was presented by Wan et al [47].…”

Section: Literature Reviewmentioning

confidence: 94%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

View full text Add to dashboard Cite

There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

show abstract

“…where "W r ,""W z ," and W represent the weights for the diverse steps of calculation. e GRU holds the same capability of producing excellent results with that of LSTM [30].…”

Section: Gated Recurrent Unitmentioning

confidence: 96%

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

Zhang

Jin

Zou

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network

Cited by 51 publications

References 21 publications

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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

Contact Info

Product

Resources

About