scholarly journals Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 214 ◽  
Author(s):  
Xue-Bo Jin ◽  
Nian-Xiang Yang ◽  
Xiao-Yi Wang ◽  
Yu-Ting Bai ◽  
Ting-Li Su ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Prevention ◽  
Fixed Number ◽  
Frequency Characteristics ◽  
Air Quality Prediction ◽  
Mode Decomposition ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

scholarly journals Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2401 ◽  
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Learning Technologies ◽  
Sensor Data ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Air Pollution Prediction ◽  
Pollution Prediction

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

scholarly journals Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5448 ◽  
Author(s):  
Sharnil Pandya ◽  
Hemant Ghayvat ◽  
Anirban Sur ◽  
Muhammad Awais ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Air Pollutants ◽  
Weather Prediction ◽  
Quality Monitoring ◽  
Prediction System ◽  
Air Quality Monitoring ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

scholarly journals Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

2022 ◽  
Author(s):  
Zhen Zhang ◽  
Shiqing Zhang ◽  
Xiaoming Zhao ◽  
Linjian Chen ◽  
Jun Yao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Temporal Difference ◽  
Short Term ◽  
Air Quality Prediction ◽  
Complex Relationships

Abstract The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

scholarly journals Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series

2021 ◽  
Vol 13 (3) ◽  
pp. 1367
Author(s):  
Taewoon Kong ◽  
Dongguen Choi ◽  
Geonseok Lee ◽  
Kichun Lee
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Real Time ◽  
Multivariate Time Series ◽  
Time Data ◽  
Time Prediction ◽  
Real Time Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on neural networks have been used. However, they usually rely on a certain model with a stationary condition, and there are few studies of real-time prediction of dynamic massive multivariate data. Use of a variety of independent variables included in the data is important to improve forecasting performance. In this paper, we proposed a real-time prediction approach based on an ensemble method for multivariate time-series data. The suggested method can select multivariate time-series variables and incorporate real-time updatable autoregressive models in terms of performance. We verified the proposed model using simulated data and applied it to predict air quality measured by five sensors and failures based on real-time performance log data in server systems. We found that the proposed method for air pollution prediction showed effective and stable performance for both short- and long-term prediction tasks. In addition, traditional methods for abnormality detection have focused on present status of objects as either normal or abnormal based on provided data, we protectively predict expected statuses of objects with provided real-time data and implement effective system management in cloud environments through the proposed method.

Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM

2020 ◽  
Author(s):  
Kabir Nagrecha ◽  
Pratyush Muthukumar ◽  
Emmanuel Cocom ◽  
Jeanne Holm ◽  
Dawn Comer ◽  
...  
Keyword(s):  
Air Pollution ◽  
Deep Cnn ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Enhancing CFD-LES air pollution prediction accuracy using data assimilation

2019 ◽  
Vol 165 ◽  
pp. 106383 ◽  
Author(s):  
Elsa Aristodemou ◽  
Rossella Arcucci ◽  
Laetitia Mottet ◽  
Alan Robins ◽  
Christopher Pain ◽  
...  
Keyword(s):  
Air Pollution ◽  
Data Assimilation ◽  
Prediction Accuracy ◽  
Using Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

2021 ◽  
Vol 18 (13) ◽  
pp. 6817
Author(s):  
Sungbo Shim ◽  
Hyunmin Sung ◽  
Sanghoon Kwon ◽  
Jisun Kim ◽  
Jaehee Lee ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
East Asia ◽  
Climate Model ◽  
Fine Particulate Matter ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Quality Controls ◽  
Quality Guidelines

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates.

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