Air Pollution Forecasting Using RNN with LSTM

Tsai, Yi-Cheng; Zeng, Yu-Ren; Chang, Yue-Shan

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00178

Cited by 147 publications

(66 citation statements)

References 11 publications

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“…ANN can incorporate complex nonlinear relationships between the concentration of air pollutants and metrological variables. Various ANN structures have been developed to predict air pollution over different study areas, such as neuro-fuzzy neural network (NFNN) [30], Bayesian neural network [31] and Recurrent neural network (RNN) [32,33]. RNN has been applied in many studies involving time-series prediction, such as traffic flow prediction [34] and wind power prediction [35].…”

Section: Air Pollutionmentioning

confidence: 99%

See 1 more Smart Citation

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics. Ensemble forecasting is more accurate than single model due to the combination of more than one model for forecasting. Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance. This paper highlights some of the challenges that are faced by the design of the ensemble model of different recurrent neural network versions, and surveys some of the most relevant works in order to give a direction of how to conduct ensemble learning research in the future. Based on the reviewed literature, we propose a framework for time-series forecasting based on the using of ensemble technique.

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Section: Air Pollutionmentioning

confidence: 99%

“…Long Short-Term Memory Unit (LSTM), is a state-of-the-art model of RNN that is recently used to predict air quality [14,15]. Many variants of RNN have been developed with different characteristics.…”

Section: Introductionmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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show abstract

“…At this time, Long Short-Term Memory (LSTM) neural network is proposed to change the network structure of RNN to overcome this defect. Tsai et al [Tsai, Zeng and Chang (2018)] adopted LSTM neural network to forecast PM2.5 concentration for next four hours in Taiwan. It was proved that used the LSTM neural network than the ANNs could had a better accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

Chen¹,

Zou²,

Liu³

2019

Computer Modeling in Engineering &Amp; Sciences

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Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) containing memory capability to implement the prediction. However, the number of neurons in the hidden layer is difficult to decide unless manual testing is operated. In order to decide the best structure of the neural network and improve the accuracy of prediction, this paper employs a self-organizing algorithm, which uses Information Processing Capability (IPC) to adjust the number of the hidden neurons automatically during a learning phase. In a word, to predict PM2.5 concentration accurately, this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration. In the experiment, not only the hourly precise prediction but also the daily longer-term prediction is taken into account. At last, the experimental results reflect that SSHL-LSTMNN performs the best.

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“…Gaganjot Kaur Kang, Jerry Zeyu Gao, Sen Chiao, Shengqiang Lu, and Gang Xie presented a review paper [10] on the big data analytics approaches and machine learning for forecasting the air quality index. According to the paper [4] it has made a research work on predicting PM 2.5 concentrations using RNN (Recurrent Neural Network) with LSTM (Long Short Term Memory) because it has been found that PM 2.5 has been severely effecting the human health and much research has been already proposed for predicting it so here RNN along with LSTM is used, which is a high level Neural Network API written in python. The data were collected from EPA (Environmental Protection Administration) [4] of Taiwan from 2012 to 2016 for forecasting data of year 2017.Here forecasting has been done for next 4 hours for 66 stations.…”

Section: Introductionmentioning

confidence: 99%

“…According to the paper [4] it has made a research work on predicting PM 2.5 concentrations using RNN (Recurrent Neural Network) with LSTM (Long Short Term Memory) because it has been found that PM 2.5 has been severely effecting the human health and much research has been already proposed for predicting it so here RNN along with LSTM is used, which is a high level Neural Network API written in python. The data were collected from EPA (Environmental Protection Administration) [4] of Taiwan from 2012 to 2016 for forecasting data of year 2017.Here forecasting has been done for next 4 hours for 66 stations. According to the paper [8] it showed a method of prediction of air pollutants of eight hours ahead of time in the area of Bilbao.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Air Pollution-A Review Paper

Raj¹

2019

IJRASET

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Traffic and industries are increasing day by day and therefore pollution is increasing rapidly as a result. In world increasing in air pollution is leading to various serious health hazards. Air pollution not only affecting humans, it had a vivid impact on the environment and aquatic life as well. This issue of air pollution has become a global concern due to its effect on nature. Thus the prediction of air pollution can be a useful step to take appropriate measures for the increasing pollution. There various measures had been reviewed in this paper which had been used for the prediction of air pollution pollutants. Some of them are Deep Learning, Machine Learning, Feed Forward Neural Network, and so on.

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Air Pollution Forecasting Using RNN with LSTM

Cited by 147 publications

References 11 publications

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

Prediction and Optimization of Air Pollution-A Review Paper

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