A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks

Mo, Yuqin; Li, Qi; Karimian, Hamed; Fang, Shuwei; Tang, Boyuan; Gong, Chen; Sachdeva, Sonali

doi:10.1016/j.atmosenv.2019.117072

Cited by 36 publications

(11 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. Table 3.…”

Section: Resultsmentioning

confidence: 99%

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%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

View full text Add to dashboard Cite

show abstract

“…The prediction method of O3 concentrations based on machine learning mainly relies on the advantage that the machine learning method can effectively capture the hidden nonlinear characteristics in the change in atmospheric composition and can build a prediction model of atmospheric composition through characteristic variables (Mo et al, 2019;Aljanabi et al, 2020;Amato et al, 2020;Betancourt et al, 2021). Machine learning models trained with data from observations or physical models can produce reliable simulations without intensive high-end computing (Ojha et al, 2021).…”

Section: A C C E P T E D Mmentioning

confidence: 99%

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Cao

Zhao²,

et al. 2023

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons.Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution.

show abstract

“…When the concentration levels of these pollutants in the air increase, they become harmful to all living things around the environment, people, and animals [10,11]. Many researchers were interested in studying the AQI and the ability of different prediction models to forecast the index using various machine learning models [12][13][14][15][16][17][18][19][20].…”

Section: Effect Of Aqi On the Financial Marketsmentioning

confidence: 99%

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

et al. 2022

View full text Add to dashboard Cite

The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts—test, validate, and train—after eliminating the outlier data. In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. The test dataset showed that R2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O3.

show abstract

A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks

Cited by 36 publications

References 58 publications

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Features Exploration from Datasets Vision in Air Quality Prediction Domain

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

Contact Info

Product

Resources

About