scholarly journals Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 209503-209516
Author(s):  
Ekta Sharma ◽  
Ravinesh C. Deo ◽  
Ramendra Prasad ◽  
Alfio V. Parisi ◽  
Nawin Raj
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Suspended Particulate Matter ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Suspended Particulate ◽  
Long Short Term Memory

scholarly journals Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020 ◽  
Vol 12 (6) ◽  
pp. 2570 ◽  
Author(s):  
Thanongsak Xayasouk ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Short Term Memory ◽  
Batch Size ◽  
Quality Data ◽  
Short Term ◽  
Optimal Learning ◽  
Term Memory ◽  
Batch Sizes ◽  
Long Short Term Memory

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India

2019 ◽  
Vol 12 (8) ◽  
pp. 899-908 ◽  
Author(s):  
Mrigank Krishan ◽  
Srinidhi Jha ◽  
Jew Das ◽  
Avantika Singh ◽  
Manish Kumar Goyal ◽  
...  
Keyword(s):  
Air Quality ◽  
Short Term Memory ◽  
Air Quality Modelling ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

2019 ◽  
Vol 10 (1) ◽  
pp. 14 ◽  
Author(s):  
Yuexiong Ding ◽  
Zheng Li ◽  
Chengdian Zhang ◽  
Jun Ma
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Quality Prediction ◽  
Short Term ◽  
Concentration Data ◽  
Term Memory ◽  
Air Quality Prediction ◽  
Long Short Term Memory

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.

PREDIKSI RATA-RATA ZAT BERBAHAYA DI DKI JAKARTA BERDASARKAN INDEKS STANDAR PENCEMAR UDARA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY

2021 ◽  
Vol 26 (1) ◽  
pp. 41-55
Author(s):  
Anisa Oktaviani ◽  
Hustinawati
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
World Health Organization ◽  
Quality Index ◽  
Short Term Memory ◽  
World Health ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Health Organization

Indonesia menempati peringkat ke-6 dari 98 negara paling berpolusi di dunia pada tahun 2019. Di tahun tersebut, rata-rata AQI (Air Quality Index) sebesar 141 dan rata-rata konsentrasi PM2.5 sebesar 51.71 μg/m3 yang lima kali lipat diatas rekomendasi World Health Organization (WHO). Salah satu kota penyumbang polusi udara yaitu Jakarta. Berdasarkan data ISPU (Indeks Standar Pencemar Udara) yang diambil dari SPKU (Stasiun Pemantau Kualitas Udara) Dinas Lingkungan Hidup DKI Jakarta melampirkan pada tahun 2019, Jakarta memiliki kualitas udara sangat tidak sehat. Oleh karena itu perlu adanya model Artificial Intelligence dalam memperdiksi rata-rata tingkat zat berbahaya pada udara di DKI Jakarta. Salah satu algoritma yang dapat diterapkan dalam membuat model prediksi dengan menggunakan data timeseries adalah Long Short-Term Memory (LSTM). Tujuan dari penelitian ini membangun model prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM yang berguna bagi para pemangku kepentingan dibidang lingkungan hidup khususnya mengenai polusi udara. Penelitian mengenai prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM, menghasilkan nilai evaluasi MAPE 12.28%. Berdasarkan hasil evaluasi MAPE yang diperoleh, model LSTM yang digunakan untuk prediksi rata-rata ISPU di DKI Jakarta masuk kedalam kategori akurat.

scholarly journals Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1172
Author(s):  
Hyunsu Hong ◽  
Hyungjin Jeon ◽  
Cheong Youn ◽  
Hyeon-Soo Kim
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Models ◽  
Short Term Memory ◽  
Air Pollutant ◽  
Short Term ◽  
Activity Data ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Mean Square Errors

Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m3 for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM2.5 air pollutant-induced damage in seaports.

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