An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model

In order to establish countermeasures for air pollution, it is first necessary to accurately grasp the air pollution state and predict the cause and change trend of the pollution situation. Due to the continuously strengthening regulations on the emissions of environmental pollutants, the forecasting and management of nitrogen oxides (NOx) emissions is receiving a lot of attention from industrial sites. In this study, a model for predicting nitrogen oxide emissions based on artificial intelligence was proposed. The proposed model includes everything from data preprocessing to learning and evaluation of the model, and used a Long ShortTerm Memory (LSTM) neural network model, one of the recurrent neural networks, to predict NOx emissions with time-series characteristics. The optimized LSTM model showed more than 93% NOx emissions prediction accuracy for both the training data and the evaluation data. The model proposed in this study is expected to be applied to the development of a model for predicting the emission of various air pollutants with time-series characteristics.

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Evolving the neural network model for forecasting air pollution time series

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2004.02.002 ◽

2004 ◽

Vol 17 (2) ◽

pp. 159-167 ◽

Cited By ~ 128

Author(s):

Harri Niska ◽

Teri Hiltunen ◽

Ari Karppinen ◽

Juhani Ruuskanen ◽

Mikko Kolehmainen

Keyword(s):

Neural Network ◽

Air Pollution ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

The Neural Network

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Long Short-Term Memory Neural Network Model for Time Series Forecasting: Case Study of Forecasting IHSG during Covid-19 Outbreak

Journal of Physics Conference Series ◽

10.1088/1742-6596/1863/1/012016 ◽

2021 ◽

Vol 1863 (1) ◽

pp. 012016

Author(s):

Raden Aurelius Andhika Viadinugroho ◽

Dedi Rosadi

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Short Term Memory ◽

Time Series Forecasting ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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A Long Short-Term Memory Neural Network Model for Predicting Air Pollution Index Based on Popular Learning

Lecture Notes in Computer Science - Database Systems for Advanced Applications. DASFAA 2020 International Workshops ◽

10.1007/978-3-030-59413-8_16 ◽

2020 ◽

pp. 190-199

Author(s):

Hong Fang ◽

Yibo Feng ◽

Lan Zhang ◽

Ming Su ◽

Hairong Yang

Keyword(s):

Neural Network ◽

Air Pollution ◽

Network Model ◽

Neural Network Model ◽

Short Term Memory ◽

Pollution Index ◽

Short Term ◽

Term Memory ◽

Air Pollution Index ◽

Long Short Term Memory

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COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements

Applied Sciences ◽

10.3390/app112210563 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10563

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Zoran Cica

Keyword(s):

Neural Network ◽

Air Pollution ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Low Cost ◽

Training Data ◽

Traffic Density ◽

The Impact ◽

Public Monitoring

In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO2 and PM10 alongside the possibility for low-cost sensor usage during this period. In the study, a device with low-cost sensors collocated with a reference public monitoring station in the city of Belgrade is used for the same period of 52 days in 2019 (pre-COVID-19 period), 2020 (COVID-19 lockdown) and 2021 (post-COVID-19 period). Low-cost sensors’ measurements are improved by using a convolutional neural network that applies corrections of the influence of temperature and relative humidity on the low-cost sensors. As a result of this study we have noticed a remarkable decrease in NO2 (primarily related to traffic density), while on the other hand CO and PM10, related to domestic heating sources and heating plants, showed constant or slightly higher levels. The obtained results are in accordance with other published work in this area. The low-cost sensors have shown a satisfactory correlation with the reference CO measurements during the lockdown, while the NO2 and PM10 measurements of 2020 were corrected using a convolutional neural network trained on meteorological and pollutant data from 2019. The results include an improvement of 0.35 for the R2 of NO2 and an improvement of 0.13 for the R2 of PM10, proving that our neural network model trained on data from 2019 can improve the performance of the sensor in the lockdown period in 2020. This means that our neural network model is very robust, as it exhibits good performance even in the case where training data from the prior year (2019) are used in the following year (2020) in very different environment circumstances—a lockdown.

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Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2019.106682 ◽

2020 ◽

Vol 186 ◽

pp. 106682 ◽

Cited By ~ 17

Author(s):

Xuanyi Song ◽

Yuetian Liu ◽

Liang Xue ◽

Jun Wang ◽

Jingzhe Zhang ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Performance Prediction ◽

Short Term Memory ◽

Well Performance ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

2020 12th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci49185.2020.9177737 ◽

2020 ◽

Author(s):

Xin Li ◽

Weijie Ren ◽

Jingying Zhao ◽

Min Han

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Network Model ◽

Neural Network Model ◽

Particle Swarm ◽

Time Series Forecasting ◽

Chaotic Time Series ◽

Swarm Optimization

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An improved self-organizing incremental neural network model for short-term time-series load prediction

Applied Energy ◽

10.1016/j.apenergy.2021.116912 ◽

2021 ◽

Vol 292 ◽

pp. 116912

Author(s):

Rong Wang Ng ◽

Kasim Mumtaj Begam ◽

Rajprasad Kumar Rajkumar ◽

Yee Wan Wong ◽

Lee Wai Chong

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Load Prediction ◽

Short Term ◽

Self Organizing

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Supervised Mover's Distance: A simple model for sentence comparison

10.7287/peerj.preprints.26847 ◽

2018 ◽

Author(s):

Muktabh Mayank Srivastava

Keyword(s):

Neural Network ◽

Simple Model ◽

Network Model ◽

Neural Network Model ◽

Short Term Memory ◽

Network Module ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Simple Neural Network

We propose a simple neural network model which can learn relation between sentences by passing their representations obtained from Long Short Term Memory(LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. Our model is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.

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